Construction of enterprise business management analysis framework based on big data technology

Abstract

With the development of science and technology, people have added a new concept big data, which is the most concerned topic at present, and has also brought great changes to the business management environment of enterprises. At present, most of the business administration work of enterprises is mainly based on human resources, and the enterprise activities are managed through the professional knowledge of relevant management personnel. However, due to human subjective factors, the management effect is unstable. Therefore, this paper designed an enterprise business management system based on intelligent data technology, and constructs an enterprise business management analysis framework. The system can help managers to make the best plan when implementing management measures, improve the efficiency of production management, sales management, financial management, personnel organization structure management, etc., so as to make business management more scientific. The experimental results showed that the improved C4.5 algorithm in the business management system proposed in this paper reduced the fuel consumption cost of shipping company A by 220.21 yuan at least and 11050.12 yuan at most, which reduced the fuel consumption cost of the company's five voyages by 13349.09 yuan in total. This indicates that the improved C4.5 algorithm has higher accuracy and better time efficiency compared to traditional C4.5 algorithms. At the same time, the optimized ship speed management effectively reduces the fuel consumption cost of flights and improves the company's operating profit. The article proves the feasibility of improved algorithms based on decision trees in enterprise business management systems, and has a good decision support effect.

1. Introduction

With the rapid development of information technology and the gradual maturity of big data technology, enterprise management is also facing more and more challenges. In this context, the construction of an enterprise management analysis framework based on big data technology has become an important direction for enterprise development. Traditional enterprise management mainly relies on manual statistics, analysis, and decision-making, which is inefficient and prone to errors. At the same time, due to the different sources and structures of data, the internal data of enterprises are often fragmentation and decentralized, which is difficult to integrate and utilize. This has had a significant impact on the efficient operation and sustainable development of enterprises. However, the emergence of big data technology has brought a series of opportunities for enterprise management. By collecting, storing, processing, and analyzing internal and external data, it is possible to gain a deep understanding and insight into the internal operations, market dynamics, customer needs, and other aspects of the enterprise, thereby providing strong support for strategic formulation, operational optimization, and product innovation. At the same time, the application of big data technology in artificial intelligence, machine learning, natural language processing and other fields also enables enterprises to conduct data mining, analysis and decision-making more efficiently, further improving the quality and efficiency of enterprise management. Therefore, the construction of an enterprise management analysis framework based on big data technology is an important direction for the current development of enterprises. By conducting in-depth mining and analysis of internal and external data, as well as comprehensive comparison and evaluation of multi-dimensional data, enterprises can be provided with more complete, accurate, and practical decision support. At the same time, this also requires enterprises to have a certain level of technical strength, data management capabilities, and talent reserves in order to maintain a competitive advantage in future market competition. This article aims to showcase the construction of a business management analysis framework based on big data technology.

The innovation of this paper is to systematically discuss the mechanism, framework and key business components of enterprise business management through big data technology, so as to build a complete theory and application system of business management model. On this basis, the business management architecture of the enterprise is studied, and its component composition and operation mechanism are studied.

2. Related work

Under the background of rapid development of science and technology, various researchers have studied and made some achievements on how to adapt to the trend of the times, maintain a leading position, and ensure competitive advantage. For example, Yohannes D conducted an empirical analysis on the financial management practices of small government enterprises in Hawassa City, and found that good financial management practices can provide a basis for small enterprises to make profits, succeed and expand [1]. Yevhen A aimed to deepen the business benefits of enterprise management in the context of organizational and economic development. A set of quantitative evaluation system for business management organization operation analysis has been established [2]. Ni H believed that business administration can make the company grow rapidly, and the innovation of enterprise management can bring new vitality to the company. He also discussed the development of enterprises, analyzed the impact of scientific and technological innovation, and discussed the implementation countermeasures of scientific and technological innovation from three levels [3]. Sycheva I N practiced and summarized the theory of employee incentive mechanism in Russia's economic development stage. He believed that by improving the proposed employee reward mechanism, the company could create a good team atmosphere and increase the trading volume, and the results obtained could provide reference for the company director to formulate plans to improve the company's management system [4]. Wang J made a comparative analysis of the management models of various countries from the perspectives of management objects, management decisions, management methods, organizational structure, etc. Finally, the basic model of enterprise dynamic management model was proposed and studied from three perspectives of perception, action and transformation [5]. Therefore, many scholars have explored different types of business management models to make up for the shortcomings of traditional management methods, but they have not systematically conducted in-depth research on business management of enterprises.

With the rapid development of network technology, the technology combined with big data technology has been widely used in corporate governance and has developed rapidly. For example, Yan Y studied the operation and management of manufacturing enterprises in the age of big data. Based on the fuzzy multi criteria decision-making method, he analyzed the performance evaluation of manufacturing enterprises, and constructed an enterprise performance management evaluation system [6]. Wan W discussed whether human resource management based on big data empowerment and authorization can effectively promote employees' internal entrepreneurship and influence the innovation performance of platform companies [7]. Li L analyzed the application of big data audit in financial sharing business, hoping to provide useful reference for enterprise management to use big data technology for big data audit [8]. Ren S studied the mechanism of big data in financial management decision-making based on information asymmetry theory and risk management theory [9]. Zou T made in-depth research and development on marketing management by using advanced technologies such as data warehouse, online analysis and data mining [10]. Smart cities use big data analysis to efficiently utilize resources, and the adoption of big data analysis faces several obstacles. The main purpose of Khan S is to investigate the main obstacles of big data analysis in the development of smart cities. To achieve this goal, 13 obstacles to using big data analysis were first identified, and then the best worst method was used for prioritization. The research results indicate that data complexity, the adoption of big data analysis frameworks, and the lack of big data analysis techniques are important obstacles hindering the integration of big data analysis into the development of smart cities [11]. As part of the Digital transformation effort, organizations are seeking more and better solutions to address long-standing enterprise content management challenges. Such solutions rarely investigate the relationship between the daily work of knowledge worker in capturing information and the perception or actual value of the information to the enterprise according to the established content management strategy. Mathieu C attempts to identify the gap between content management practices and policies by modeling the practices of an organization's knowledge worker who usually generate, store, and later recover their daily work products. Mathieu C outlines various permutations of digital object creation, description and storage models, and provides basic strategies to keep the values of knowledge worker consistent with the institutional directives to improve the findability and reusability of enterprise content [12]. It can be seen that many technologies and theories related to big data technology are still conceptual, so it still needs to take some time for big data technology to be truly put into practice.

3. Design of enterprise business management system

3.1. Enterprise management

Enterprise management refers to the comprehensive and systematic management of various strategies and decisions formulated by enterprises in order to achieve expected goals in their daily business activities. These strategies and decisions include finance, human resources, marketing, supply chain management, etc., aimed at optimizing enterprise resource allocation, improving production efficiency and profitability, as well as responding to market changes and risk management. Successful business management requires attention to the following aspects:

Firstly, there must be a clear business strategy and objectives. A company needs to clarify its mission and vision, set long-term and short-term business goals, and develop corresponding plans and implementation plans. Only through clear business strategies and goals can it help enterprises carry out long-term planning and effective resource allocation, ensuring that they are on the right path.

Secondly, attention should be paid to employee training and development. Employees are the core assets of a company, and their qualities and abilities are crucial for the development of the company. Therefore, enterprises need to provide employees with good training and development opportunities to help them grow into professional talents with innovative spirit, teamwork ability, and efficient execution, thereby further enhancing the core competitiveness of the enterprise.

Thirdly, we need to strengthen marketing and brand building. Marketing and brand building are important means of communication between enterprises and the outside world, and are also key factors to ensure the long-term development of enterprises. Therefore, enterprises need to actively conduct market research, understand consumer demand and market trends, develop appropriate marketing strategies and brand plans, and ensure the effectiveness of marketing activities and cost control.

Fourthly, attention should be paid to supply chain management and risk control. Supply chain management is an important means of coordinating internal and external resources in enterprises, involving multiple links such as procurement, production, warehousing, logistics, etc. In these links, various potential risks and problems may arise, such as unstable supplier quality and inventory backlog. Therefore, enterprises need to establish a sound risk management mechanism, continuously optimize supply chain processes and management methods, and ensure the operational efficiency and quality level of the enterprise.

In short, enterprise management needs to consider multiple factors, starting from strategic formulation and goal planning, to employee training and development, marketing and brand building, supply chain management, and risk control, all of which require systematic management and innovation. Only by continuously optimizing management methods and processes can we maintain a leading position in the increasingly fierce market competition and achieve long-term sustainable development.

3.2. Concept of business administration

The company's industrial and commercial administration is responsible for the maintenance, updating, development, adjustment and post standards of the organization. At the same time, the enterprise management department supervises the establishment and implementation of power, and improves the abuse of power. The management structure is shown in Fig. 1.

Fig. 1.

Enterprise business management structure.

It can be seen from Fig. 1 that the management level of enterprise business management consists of three major parts. Under the grass-roots management, it can be divided into five departments. The technology and quality department is responsible for the product research and technology development of the enterprise. The logistics procurement department is responsible for the procurement of raw materials for production and supplies for daily use in offices. The marketing department is responsible for the product marketing operation of the enterprise and belongs to the key department of the company. The financial department is responsible for the financial statistics of all activities of the company, and the administrative logistics department is responsible for the daily logistics management of the company. These management departments are collectively referred to as business administration of enterprises to jointly maintain the operation of a company. To make a company run well and long, a scientific management system is required to support the operation of each department [13].

3.3. Design of system architecture

Generally, the management system completes the setting of display layer and data layer on the workstation, and can only be stored in the file server sharing the database [14,15]. When the server receives a message, the database processes it after receiving it, searches the server database, sends the processing architecture to the server, and submits the processing results to the database for updating. The system development mode usually includes two architectures: client/server (C/S) and browser/server (B/S) [16].

As can be seen from Fig. 2, the application is divided into two different customer service processes. The front end is a user interface based on data processing. The client of C/S architecture sends requests to the server, processes them on the server side, and displays the returned results. The requests sent from clients on the Web are mainly from the server and returned. The client interface in C/S mode is friendly, and database maintenance is simple, so the response speed and processing ability are strong [17]. B/S architecture is an innovation and development of C/S, which is a Web based application service and response to browser request service. The B/S structure can be built on the interface level between users and the system. The enterprise business management system structure designed in this paper adopts B/S structure [18,19]. The system is composed of data storage layer, business logic layer, middleware layer and business presentation layer. The B/S layer is a three-tier architecture, with the browser as the client. The browser requirements are implemented by a B/S architecture server. Fig. 3 shows the application architecture of the enterprise business administration system.

Fig. 2.

Block diagram of system development mode.

Fig. 3.

Application architecture of enterprise business management system.

The company's industrial and commercial management system structure is a three-layer structure of data layer, business logic layer and user presentation layer. They are logically connected, but they are mutually independent in unit components. In the enterprise business management system, the Web and application server are used to complete all the work through the application server [20,21]. The Web server responds to the user's request and feeds back the server's processing results to the client. The application software in the system is presented in the form of B/S, and the interface is designed with unified standards. The application server accesses databases and file servers at different physical locations through specific protocols, provides unified services for all complex working application servers, and processes them. The three-layer structure of the enterprise business management system is shown in Fig. 4.

Fig. 4.

Three-layer structure of management system.

In the process of using the enterprise business management system, the enterprise organically integrates the flow of people, information, technology, decision-making and materials to form a supply chain management model that integrates internal and external management, which can not only achieve the short-term and long-term benefits of the enterprise, but also achieve the maximum local and global benefits of the enterprise [22,23]. Business process reengineering reduces unnecessary costs and labor intensity and inventory by using standardized production processes, improves work efficiency and quality, and timely adjusts production and makes optimal resource allocation according to customer requirements. The basic functions of the system are shown in Fig. 5.

Fig. 5.

Basic functions of enterprise business administration system.

Financial management: Based on the financial information, the company conducts unified management on the company's asset items such as accounts receivable and accounts payable, so as to reduce financial risks and ensure the company's financial capital flow. Cost management: It mainly uses information such as product structure, production mode, work center, procurement and process, and unified financial reporting system to conduct cost accounting and analysis for enterprises, so as to master the company's profitability. Sales management: This mainly refers to sales statistics, and maintains the relationship with customers. Production management: It is plan-oriented and production-oriented to reduce inventory and improve production efficiency. The extension function is mainly used to integrate and optimize these departments in many aspects, so as to help managers and employees of enterprise departments make the best decisions.

3.4. Construction of enterprise management analysis framework

This paper adopts a two-layer distributed decision-making model to analyze the business management problems of enterprises, while the high-level model makes decisions on the risk control level of each manager. The lower level model is used to make decisions on the optimal management method. The mathematical model is described in detail below. If the number of managers is M and $C_m$ is the best management measure, the upper level model is expressed as:

$$\max \left\{G\prod _{m=1}^M{O}_m\left(R_m\right)-\sum _{m=1}^M{C}_m\left(R_m\right)\right\}$$ (1)

$$\begin{array}{c}s.t\text{:}\\ \end{array}\begin{array}{cc}& c_{ml}\le C_m^{\max }\end{array}$$ (2)

In (1), (2), $O_m\left(R_m\right)=\left\{O_{ml}/R_m=l,l=\mathrm{1,2},...,V^{\max }\right\}$ and $C_m\left(R_m\right)=\left\{c_{ml}/R_m=l,l=\mathrm{1,2},...,V^{\max }\right\}$. $O_m\left(R_m\right)$ is the influence function of the m-th manager's decision on the general management of the enterprise, and $C_m\left(R_m\right)$ is the mth manager's control decision function. $R_m$ refers to the mth management decision risk control level, and G refers to the total income of the enterprise without risk. The lower layer model is expressed as:

$$\min \left\{\sum _{n=1}^{N_m}\sum _{i=1}^{B_{mn}}\left(x_{mni}^t{d}_{mni}^t\right)\right\}$$ (3)

$$\begin{array}{c}s.t:\begin{array}{cc}& \sum _{n=1}^{N_m}\sum _{i=1}^{B_{mn}}\left(x_{mni}^t{d}_{mni}^t\right)\end{array}\\ \end{array}\le C_m^{\max }$$ (4)

In (3), (4), $N_m$ is the number of decision-making risks of the mth manager, and $B_{mn}$ is the number of the nth management measures of the mth manager. $d_{mni}^t$ is the score of the tth item of the ith management measure for the nth risk of the mth manager. $x_{mni}^t$ is the index of the tth project of the ith management measure of the nth risk used by the mth manager.

In distributed decision-making, the high-level can predict the preferences of the low-level through a variety of methods, thus obtaining different prediction models of the low-level. A complete prediction method is used to predict the underlying patterns. In Fig. 6, in the upper layer model, through the preference for the lower layer model and the management information of each stage, the management control reservation measures of each stage are determined and transferred to the actual lower layer model. The lower level model determines the best business management strategy of the enterprise according to the control reservation measures and the actual operation conditions of each stage.

Fig. 6.

Model of enterprise management risk.

In the business administration of enterprises, there are often many problems that require managers to make corresponding decisions, so this paper uses the classic decision classification algorithm to calculate managers' decisions. ID3 (Iterative Dichotomiser 3) algorithm is one of the commonly used classification algorithms. The key to ID3 algorithm is to select the attribute with the maximum information gain value, so that it has the maximum information gain, that is, in each branch, the gain rate of each attribute needs to be calculated and the maximum branch needs to be obtained. If A is the divided industrial and commercial management data, the entropy of A is:

$$I_O\left(A\right)=-\sum _{i=1}^m{b}_i{\log }_2\left(b_i\right)$$ (5)

In Formula 5, $b_i$ is the probability of the ith factor in the whole management data set. A is subdivided by attribute C, and the expected value of C for A is expressed as:

$$I_{OC}\left(A\right)=\sum _{j=1}^m\frac{\left|A_j\right|}{\left|A\right|}{I}_o\left(A_j\right)$$ (6)

In Formula 6, $A_j$ is the expected number of factors of type j in the management data set, and the information gain g is the difference between entropy and expected value:

$$g\left(C\right)=I_O\left(A\right)-I_{OC}\left(A\right)$$ (7)

ID3 algorithm is a top-down construction of decision tree. It is the simplest method and uses methods that cannot be returned. The purpose of ID3 algorithm is to reduce the depth of the tree, while ignoring the analysis of the number of leaves. ID3 algorithm is only applicable to discrete data, so on the basis of ID3 algorithm, C4.5 algorithm is obtained after optimization. Its split information SI is defined as:

$$S{I}_{OC}\left(A\right)=-\sum _{j=1}^V\frac{\left|A_j\right|}{\left|A\right|}{\log }_2\left(\frac{\left|A_j\right|}{\left|A\right|}\right)$$ (8)

Then the gain rate is:

$$gr\left(C\right)=\frac{g\left(C\right)}{S{I}_O\left(C\right)}$$ (9)

The main goal of this method is to extract significant attributes from a large number of attributes. Then, by building a problem-based mining model, the role mining is realized and displayed. Although the C4.5 algorithm improves the ID3 algorithm, due to the defects of too many empty branches, the decision tree is not robust enough, and the dataset has to be scanned and sorted for many times, the efficiency of it is greatly reduced. On this basis, this paper designs an improved C4.5 algorithm, which can maximize the entropy value in the branches of the decision tree, merge the branches with less important classification, and then calculate the entropy of each branch to determine the characteristics of each branch. The basic process of the improved C4.5 algorithm is to preprocess the business management data and average or ignore the missing data. On this basis, the entropy values of each subset are counted and compared. Then, the branch node is taken as the root node, and the next level node and branch structure are selected by recursion method.

This paper mainly introduces the design of enterprise business management system, including business management concept, system architecture design and the construction of enterprise management analysis framework. The enterprise business management system adopts B/S structure and consists of data storage layer, business logic layer, middleware layer and business presentation layer. The basic functions of this system include financial management, cost management, sales management, and production management. In the construction of the enterprise management analysis framework, this article adopts a two-layer distributed decision model to analyze the business management problems of the enterprise. The ID3 algorithm and C4.5 algorithm are used to classify the decisions of managers and determine the optimal business management strategy. Based on this, conduct testing and analysis on the enterprise business management system.

4. Application test of enterprise business management system

4.1. Experimental design

Experimental objectives: Evaluate the efficiency and accuracy of C4.5 algorithm and improved C4.5 algorithm in constructing decision trees on enterprise business management datasets. Compare the impact of optimized speed on the cost and profit of shipping companies.

Experimental Step: Data Preparation: Collect the business management dataset of shipping companies, including voyage, port information, fuel prices, etc. Prepare a dataset for algorithm testing, including enterprise business management data from different groups. Algorithm testing: Use C4.5 algorithm and improved C4.5 algorithm to test different groups of enterprise business management datasets. Record the average time and accuracy required for each algorithm to construct a decision tree. Model solution and results: Taking A city shipping company as an example, the improved C4.5 algorithm is applied to optimize the management of ship speed. Calculate the optimized ship speed and compare it with the pre optimized ship speed. Compare the fuel consumption cost of the voyage before and after optimization, and calculate the increase in operating profit.

4.2. Comparison of algorithm testing

The C4.5 algorithm and the improved C4.5 algorithm proposed in this paper were used to test different groups of enterprise business administration data sets, and the average time and accuracy required to build a decision tree by the C4.5 algorithm and the improved C4.5 algorithm were compared and analyzed, as shown in Fig. 7.

Fig. 7.

Efficiency comparison between C4.5 algorithm and improved C4.5 algorithm.

It can be seen from Fig. 7(a) that the usage time of both algorithms was on the rise with the increase of data volume. However, with the same amount of data, C4.5 algorithm took longer than the improved C4.5 algorithm. When the number of data reached 1400, the average time of C4.5 algorithm was 386 ms, while the average time of the improved C4.5 algorithm was 364 ms. It can be seen from the comparison chart in Fig. 7(b) that the calculation accuracy of the two algorithms increased with the increase of the sample size, but the accuracy of the two algorithms was still different. When the amount of data was small, there was little difference between the accuracy of the two algorithms, while when the number of data was large, there was significant difference between the accuracy of the two algorithms. It can be seen that the efficiency of the improved C4.5 algorithm was better than that of the C4.5 algorithm.

4.3. Model solution and results

This paper took shipping company A in a city as an example to study whether scientific decision-making can achieve scientific management and enhance its core competitiveness. The data came from the actual data of the shipping company. Five ships of the company were selected for the experiment, which were recorded as voyage No. 101–105 respectively, and there were 10 ports to pass through, which were recorded as A1–A10 respectively. The basic information of its port is shown in Tables 1 and 2.

Table 1.

Price of port fuel.

Name of port	Fuel Price	Name of port	Fuel Price
A1	7.06	A6	7.08
A2	7.38	A7	7.23
A3	7.04	A8	7.22
A4	7.06	A9	7.09
A5	7.23	A10	7.09

Table 2.

Mileage from the starting point of the port.

Starting port	Terminal port	Miles (nautical miles)
A1	A3	195
A3	A9	88
A2	A1	704
A5	A10	635
A10	A8	1492
A2	A4	275
A3	A6	91
A6	A7	851

The above improved C4.5 algorithm is used to optimize the management of the company's ship speed. The optimized speed is shown in Table 3. For example, among the five voyages, the average speed of 102 voyages before optimization is 25.24 nautical miles/hour, and the average speed after optimization is 25.13 nautical miles/hour. The speed after optimization decreases the least. For 105 voyages, the ship's speed before optimization is 26.51 nautical miles/hour, and the speed after optimization is 26 nautical miles/hour. The speed decreases the most in all voyages.

Table 3.

Information of voyage.

Voyage number	Departure time	Time of arrival	Voyage description	Speed (knots)	Optimum speed
101	12–12	12–23	A1–A3–A9	24.13	23.70
102	12–10	12–18	A2–A1–A8	25.24	25.13
103	12–9	12–23	A5–A10–A8	24.35	24
104	12–8	12–16	A2–A4–A2	23.46	23
105	12–6	12–23	A3–A6–A7	26.51	26

Through solving the C4.5 improved algorithm, the optimal speed of each voyage was applied to the company's ship operation and management, and the previous single voyage consumption cost statistics was compared with the optimized single voyage cost statistics results.

As shown in Fig. 8, through comparison, it is found that the fuel consumption cost of each voyage after optimizing the speed has decreased. The fuel consumption cost of voyage 101 decreased by 799.97 yuan. The fuel consumption cost of voyage 102 and 104 decreased nearly to 220.21 yuan and 269.1 yuan respectively. The fuel consumption cost of voyage 103 was reduced by 11050.12 yuan, which was the largest. This meant that the operating profit of the company's five voyages increased by 13349.09 yuan in total.

Fig. 8.

Comparison of fuel costs.

5. Discussions

In the comparison part of the algorithm test in the experiment, under the same amount of data, the C4.5 algorithm takes longer than the improved C4.5 algorithm. When the data volume is at the initial 200, the average time of the two algorithms is below 320 ms, while when the data volume reaches 1400, the time of the two algorithms is more than 360 ms. In the accuracy comparison test of the two algorithms, the improved C4.5 algorithm has always been ahead of the traditional C4.5 algorithm. In the comparison experiment of fuel cost, the fuel consumption cost before and after optimization is similar, and the fuel consumption cost after optimization is slightly less than that without optimization. However, in the 103 flights, the fuel cost before optimization is about 110,010 yuan, but the fuel cost after optimization is as low as 98,960 yuan. This proves that this paper proposes an improved C4.5 algorithm based on decision tree to be applied to the development of enterprise management system, which can provide an effective basis for managers' decision support. This proves the feasibility of designing an enterprise business management system based on intelligent data technology.

This article discusses in detail the use of C4.5 algorithm and improved C4.5 algorithm in enterprise business management. However, with the continuous development and popularization of artificial intelligence technology, there are many other decision support tools that can be applied to enterprise business management systems.

For example, algorithms based on neural networks can simulate human intelligent decision-making processes, analyze and process data, and output corresponding recommendation results. In addition, support vector machines, Bayesian network and other algorithms can also be used for tasks such as classification, prediction and monitoring. Therefore, when designing and developing enterprise business management systems, it is necessary to select suitable algorithms based on different needs and scenarios, and pay attention to the quality and accuracy of data in the implementation process of algorithms to improve the effectiveness and reliability of decision support.

In addition to algorithm selection and data quality assurance, it is also necessary to focus on user experience and ease of use. Enterprise business management systems typically need to consider multiple factors, such as interface design, functional settings, and operational processes. Therefore, in order to improve user satisfaction and use effect, it is necessary to consider user perspectives and needs in system design, and adopt appropriate UI design, interaction design, user research and other methods.

In summary, as a complex decision support system, the enterprise business management system needs to comprehensively consider multiple factors, including algorithm selection, data quality, user experience, etc. Only by doing well in these aspects can we truly improve the decision-making level and operating profit of the enterprise, and inject new impetus into its development.

6. Conclusions

From the background of big data, this paper has discussed the specific measures of enterprise business management, and constructs an enterprise business management model. According to the actual situation of the current enterprise management mode, the problems and defects can be found out, and the large amount of safety production information generated in the big data era can be used to achieve scientific data and platform based management. This article uses the C4.5 algorithm and the improved C4.5 algorithm to test different groups of enterprise business management datasets, and finds that the efficiency of the improved C4.5 algorithm is better than that of the C4.5 algorithm. Meanwhile, taking A city shipping company as an example, this article uses the C4.5 improved algorithm to optimize the management of ship speed and applies the optimal speed of each voyage to the company's ship operation management. It is found that the optimized speed reduces fuel consumption costs and improves the company's shipping operating profit. Therefore, it can be seen that the C4.5 improved algorithm has good application prospects in the application testing of enterprise business management systems, and can help enterprises improve management efficiency and revenue levels.

In the future, with the continuous development and application of artificial intelligence technology, more and more decisions in enterprise business management systems will rely on the support of artificial intelligence algorithms. The C4.5 improved algorithm will also be widely applied and improved in this context. For example, the algorithm can be optimized and upgraded to adapt to more complex business scenarios. At the same time, applying AI algorithms to enterprise business management still needs to continue to address some issues, such as how to ensure the quality and accuracy of data, and how to address data security issues. Therefore, research and improvement in these areas are needed in the future to promote innovation and enhancement of enterprise business management systems. In summary, the C4.5 improved algorithm, as an effective decision support tool, will play an important role in future enterprise business management.

Author contribution statement

Jinqian Peng: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

Liyuan Bao: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding

This study did not receive any funding in any form.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Additional information

No additional information is available for this paper.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.