# Research on Enterprise HRM Effectiveness Evaluation Index System Based on Decision Tree Algorithm

Authors: Junguo Chen; Shasha Chen; Wanli Wang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In order to explore the application analysis of the decision tree algorithm in enterprise HRM effectiveness evaluation, we discuss data mining, decision tree algorithm, and decision tree model construction. In view of the continuous growth of big data information in the information age, we use the mean clustering algorithm, expert system algorithm, and decision tree algorithm to verify the accuracy and time efficiency of enterprise HRM effectiveness. Finally, it is considered that the application of the decision tree algorithm of data mining not only is in line with the enterprise HRM efficiency evaluation but also can promote the integration and effective allocation of human resources and transform the generated decision tree algorithm into classification rules, which has been widely studied and applied, and finally has important strategic significance for the development of enterprises.


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## Body

## 1. Introduction

With the progress of science and technology, enterprises pay more and more attention to human resources, and the requirements for talents are also more and more strict. Whether an enterprise’s employees are excellent or not depends not only on their personal quality but also on the incentive effect of the enterprise incentive system. If the incentive measures are appropriate, the proportion of employees with certain personal quality who naturally become excellent employees will be higher. In the past, with regard to human resource management, due to various reasons, the data update is slow, and there are certain difficulties in statistics, which creates a series of problems with regard to the issue of talents. Nowadays, with the continuous progress and development of science and technology, artificial intelligence has been widely used in various fields. Among them, the decision tree algorithm has been used in enterprise human resource management. In the research of Huang and Liu, it is pointed out that in order to meet the development of enterprises and talent evaluation technology, the decision tree algorithm is used to analyze and study human resources and enterprise management, so as to solve the problems in human resource management [1]. The decision tree algorithm is a typical classification method. Firstly, it processes the data, uses inductive algorithm to generate readable rules and decision tree, and then uses decision to analyze new data. In essence, decision tree is a process of classifying data through a series of rules. The decision tree algorithm not only is applied in enterprises but also plays an important role in the field of nursing. Xu and Zhang analyzed its application progress by using the decision tree algorithm on human resource management and risk management in nursing [2]. In our life, the microgrid protection method based on the previous systems and algorithms cannot meet the current situation. According to Hu et al., the decision tree algorithm is used to protect and test the microgrid voltage, so that any fault can be accurately detected [3].Enterprises not only need to solve the problem of human resource management but also face all kinds of customers. How to solve the customer relationship has become an important problem. In the Lai [4] research guide, the decision tree algorithm is used to study and analyze the management of enterprise customers. Through the decision tree algorithm, enterprises can help solve customer relations faster and make better decisions [4]. In the information society, there are deficiencies in different data analyses and statistics. In order to improve the accuracy and efficiency of data mining, Li [5] studied information system data mining and analyzed and discussed it by using decision tree algorithm. The classification accuracy of different data is higher than 95%, and the efficiency of data mining has been improved [5]. The decision tree algorithm is applied in the information society and also in the field of industrial production. China is a large manufacturing country, which is inseparable from the operation of production lines. In the previous calculation of the balance rate of production lines, it was found that the imbalance degree of production lines is large. For its existing problems, Wang et al. analyzed and studied the imbalance of production lines by using the decision tree algorithm in scientific and technological innovation and finally improved the balance rate of production lines to obtain the final grouping method [6]. With the rapid development of the network, some bad businessmen use the network to have a certain impact on their environmental security, resulting in a series of network environmental security problems. With regard to how to solve the security problem, Wang et al. studied the existing problems and introduced the decision tree algorithm to classify the traffic and prevent it from being threatened by malicious attackers, providing better spatial conditions for the transmission of various types of information data [7]. Compared with the previous methods of human resource recommendation, the process is complex, the recommendation error is large, and it is not easy to meet the requirements of enterprises. Now, we want to improve the accuracy of human resource recommendation. Sun’s research shows that the use of the decision tree algorithm greatly improves the results of human resource recommendation [8]. With the development of society and the continuous innovation of scientific research technology, this research is based on the full application of the decision tree algorithm in all walks of life. It uses the efficiency evaluation method and efficiency verification to analyze and explore. At the same time, it is necessary for the country to make progress without the joint efforts of enterprise talents. Enterprises use the decision tree algorithm to improve the HRM efficiency evaluation, which does not only lay a foundation for enterprises to retain talents. At the same time, it also promotes the development of the national economy.


## 2. Classification and Review of Enterprise HRM Effectiveness Evaluation Methods

The purpose of enterprise HRM is to integrate the knowledge quality, skill quality, physical quality, and character quality of employees, fully mobilize the potential and creativity of employees, and effectively support the overall development strategy of the enterprise. The evaluation methods of human resource management effectiveness can be divided into audit evaluation, subjective evaluation, index evaluation, cost-benefit evaluation, and system evaluation methods. Now, these five evaluation methods are reviewed. Audit evaluation mainly refers to the final evaluation results obtained by comparing and analyzing the actual completion of human resource work with the established objectives. Human resource audit determines the work completion plan determined at the beginning of organization management, compares with the effectiveness and work plan formulated in the process of human resource management, and evaluates HRM efficiency by collecting data, summarizing results, and analyzing and mining data. The involvement of human resource audit can further improve the efficiency of human resource work and effectively ensure the normal development of work. The evaluation results are found to be practical. Subjective evaluation is to evaluate enterprise HRM effectiveness through employee attitude and employee satisfaction with human resource work. Among them, employee motivation and attitude are important influencing factors of organizational performance and important objects of human resource work. According to the final survey and analysis results, part of the effectiveness of HRM can be measured. This method can integrate employee attitude and organizational performance and realize the evaluation of enterprise human resource work. Although this evaluation method has some subjectivity, it is of great significance to the evaluation of overall effectiveness. Index evaluation refers to the direct selection of indicators for objective evaluation. Recruitment, training and development, performance evaluation, and salary relationship are taken as the measurement standards, as well as the turnover rate and employment rate of employees. This evaluation method can make it easy for enterprises to find the gap with other enterprises, improve in time, promote the better development of enterprises, and have an obvious guiding role in evaluating the contribution rate through the indicators of labor productivity, quality, and employee relations. The index evaluation method has been proven to be effective by many enterprises. This method is easy to calculate and understand and is widely accepted by mass enterprises. Cost-benefit evaluation takes value as an important standard to compare the cost and benefit of human resources and evaluate the efficiency of HRM. Various analysis methods are used to make HRM efficiency more intuitive. Measuring enterprise HRM efficiency through the cost-benefit evaluation method has high credibility. The final evaluation result is also very intuitive, which is conducive to the improvement of human resource competitiveness. The systematic evaluation method is guided by the system and integrates the previous evaluation methods and indicators, so as to systematically and comprehensively evaluate the enterprise HRM efficiency; further promote the enterprise development and tap and develop human resources according to the enterprise human resources, knowledge management, and enterprise development; and improve the enterprise HRM efficiency evaluation system with regard to adaptability, execution, and effectiveness. The five evaluation methods study the evaluation of human resource management effectiveness from multiple angles. Each algorithm has its own advantages and disadvantages, as well as the independence of each algorithm. If the algorithm is not appropriate, it will reduce the scientificity and availability of the final evaluation results.


## 3. Application of Decision Tree Algorithm in Enterprise HRM Efficiency Evaluation Index System

### 3.1. Data Mining Process

As the saying goes: a good beginning is half the success. For enterprise managers, good decision-making is also an important part of the success of enterprise management. Moreover, good decision-making is more conducive to good decision-making methods. With the rapid development of information technology and increasingly fierce market competitiveness, enterprise managers can increasingly feel the importance of speed to enterprises. Under the background of the great development of the information network era, the HRM human resource management system takes root in enterprises, which not only gives HR personnel more time and energy for strategic thinking but also builds a standardized, standardized, and networked working platform, so as to connect the different roles in the ecological chain of human resource management and truly realize the “comprehensive human resource management” of enterprises. Both domestic enterprises and enterprises in developed countries need a more advanced and more accurate human resource management system. Nowadays, human resources have gradually become the core of world economic development. It can be seen that human resources occupy an important leading position in the rapid economic development.While using the database, the enterprise human resource data increases sharply. Sorting out the previous human resource management is mainly based on the subjective experience of managers, which is prone to mistakes. When collecting human resource data, in the face of the information in the database, the decision tree can be used to quickly and deeply analyze and process these information, find the laws, and obtain relevant useful information, so as to provide the basis for enterprise HRM decision-making, Now, the frame diagram of the decision tree algorithm is explained and analyzed, as shown in Figure1.Figure 1
Flow framework of data mining.Figure1 shows the flow framework of data mining. Human resource data comes from a wide range of sources. After processing the collected data, we put all the data into the database, preprocess the data, and filter the system. All the data information is allocated to the appropriate list. Deleting useless data can reduce and shorten the working time. In the process of retaining key information extraction, it can mine useful relevant information, Finally, appropriate data will be reserved for storage, and appropriate information will be outputted.


### 3.2. Basic Framework of Decision Tree

An important responsibility of contemporary enterprise managers is scientific decision-making. In the practice of enterprise management, there are certain uncertain factors for each scheme formulation, and there will be a variety of different results, which has a certain risk for enterprise decision-making. With the progress and development of the information age, the decision tree algorithm has been widely used in enterprise management and the human resource management system, which can effectively control the risks brought by decision-making and optimize the human resource management system through final calculation. In the enterprise HRM effectiveness evaluation system, the basic structure of a complete data mining decision tree is shown in Figure2.Figure 2
Basic frame structure of decision tree.Figure2 shows the basic framework structure of the decision tree. In data mining, first, filter the information in the human resource database and search and query the important fields of the data, which is easy to extract. The filtered data is trained to generate the decision tree and then prune the nodes and leaves of the decision tree. This can improve the work efficiency, find the rules in decision classification, and mine the corresponding useful information, which completes the visual output of the final data during decision evaluation.


### 3.3. Model Construction of Decision Tree

The decision tree is more and more widely used in human resource management. Through the correlation analysis of decision tree theory, the primary data are screened and satisfactory results are obtained. This method is a general and easy-to-understand method in data mining. It is used for the classification and prediction of new data. It is a tree structure similar to a flow chart, to describe the result prediction of each scheme node in the future. Each situation is possible. When it is unpredictable, the probability of various natural states can be inferred according to the existing data, as shown in Figure3.Figure 3
Structure diagram of decision tree model.Figure3 shows the model structure diagram of the decision tree, which can intuitively apply probability analysis. The process of the decision tree starts from the fundamental problem of decision-making, outputs the branch of the starting point of decision-making to the scheme node, takes the storage category of the scheme node as the decision result, and finally gets a satisfactory decision.


### 3.4. Algorithm Formula

(1)
Use the decision tree method to construct the “scoring rules and measures” for each value of the indexzj; the calculation formula is as follows:(1)zj=W×Rj∑i=1mrjm.Rj=rj1,rj2,⋯,rjmr, which corresponds to an indicator valuejincluding the evaluation value ofVmwhich is the number of objects.
(2)
According to the “measurement value of scoring rules,” a formula can be constructed to finally determine the score value of each value of each indicatorλj(2)λj=INTzjMaxj=1nzj×yj,where n is the number of indicator values, yj is the weight of the evaluation index, and INT represents the integral function.It is considered that the evaluation results of the HRM algorithm are consistent with the evaluation results in the application of decision-making rules.


## 3.1. Data Mining Process

As the saying goes: a good beginning is half the success. For enterprise managers, good decision-making is also an important part of the success of enterprise management. Moreover, good decision-making is more conducive to good decision-making methods. With the rapid development of information technology and increasingly fierce market competitiveness, enterprise managers can increasingly feel the importance of speed to enterprises. Under the background of the great development of the information network era, the HRM human resource management system takes root in enterprises, which not only gives HR personnel more time and energy for strategic thinking but also builds a standardized, standardized, and networked working platform, so as to connect the different roles in the ecological chain of human resource management and truly realize the “comprehensive human resource management” of enterprises. Both domestic enterprises and enterprises in developed countries need a more advanced and more accurate human resource management system. Nowadays, human resources have gradually become the core of world economic development. It can be seen that human resources occupy an important leading position in the rapid economic development.While using the database, the enterprise human resource data increases sharply. Sorting out the previous human resource management is mainly based on the subjective experience of managers, which is prone to mistakes. When collecting human resource data, in the face of the information in the database, the decision tree can be used to quickly and deeply analyze and process these information, find the laws, and obtain relevant useful information, so as to provide the basis for enterprise HRM decision-making, Now, the frame diagram of the decision tree algorithm is explained and analyzed, as shown in Figure1.Figure 1
Flow framework of data mining.Figure1 shows the flow framework of data mining. Human resource data comes from a wide range of sources. After processing the collected data, we put all the data into the database, preprocess the data, and filter the system. All the data information is allocated to the appropriate list. Deleting useless data can reduce and shorten the working time. In the process of retaining key information extraction, it can mine useful relevant information, Finally, appropriate data will be reserved for storage, and appropriate information will be outputted.


## 3.2. Basic Framework of Decision Tree

An important responsibility of contemporary enterprise managers is scientific decision-making. In the practice of enterprise management, there are certain uncertain factors for each scheme formulation, and there will be a variety of different results, which has a certain risk for enterprise decision-making. With the progress and development of the information age, the decision tree algorithm has been widely used in enterprise management and the human resource management system, which can effectively control the risks brought by decision-making and optimize the human resource management system through final calculation. In the enterprise HRM effectiveness evaluation system, the basic structure of a complete data mining decision tree is shown in Figure2.Figure 2
Basic frame structure of decision tree.Figure2 shows the basic framework structure of the decision tree. In data mining, first, filter the information in the human resource database and search and query the important fields of the data, which is easy to extract. The filtered data is trained to generate the decision tree and then prune the nodes and leaves of the decision tree. This can improve the work efficiency, find the rules in decision classification, and mine the corresponding useful information, which completes the visual output of the final data during decision evaluation.


## 3.3. Model Construction of Decision Tree

The decision tree is more and more widely used in human resource management. Through the correlation analysis of decision tree theory, the primary data are screened and satisfactory results are obtained. This method is a general and easy-to-understand method in data mining. It is used for the classification and prediction of new data. It is a tree structure similar to a flow chart, to describe the result prediction of each scheme node in the future. Each situation is possible. When it is unpredictable, the probability of various natural states can be inferred according to the existing data, as shown in Figure3.Figure 3
Structure diagram of decision tree model.Figure3 shows the model structure diagram of the decision tree, which can intuitively apply probability analysis. The process of the decision tree starts from the fundamental problem of decision-making, outputs the branch of the starting point of decision-making to the scheme node, takes the storage category of the scheme node as the decision result, and finally gets a satisfactory decision.


## 3.4. Algorithm Formula

(1)
Use the decision tree method to construct the “scoring rules and measures” for each value of the indexzj; the calculation formula is as follows:(1)zj=W×Rj∑i=1mrjm.Rj=rj1,rj2,⋯,rjmr, which corresponds to an indicator valuejincluding the evaluation value ofVmwhich is the number of objects.
(2)
According to the “measurement value of scoring rules,” a formula can be constructed to finally determine the score value of each value of each indicatorλj(2)λj=INTzjMaxj=1nzj×yj,where n is the number of indicator values, yj is the weight of the evaluation index, and INT represents the integral function.It is considered that the evaluation results of the HRM algorithm are consistent with the evaluation results in the application of decision-making rules.


## 4. Algorithm Efficiency Verification

### 4.1. Analysis on the Accuracy of Different Algorithms for Enterprise HRM Effectiveness Evaluation

With the gradual maturity of data mining technology, the decision tree is widely used in enterprise HRM effectiveness evaluation. As one of the important algorithms in the field of data mining, the algorithm has three important components in intelligent decision-making, including feature, algorithm, and decision-making. When facing problems, the decision tree can deal with the problems of missing data and many fields very steadily. Compared with other algorithms, the decision tree is easier to understand, simple, and intuitive to analyze. Now, aiming at the ability quality, skill quality, physical quality, knowledge quality, and character quality, the mean clustering algorithm, expert system algorithm, and decision tree algorithm are used to compare the accuracy of enterprise HRM efficiency evaluation, as shown in Table1.Table 1
Comparison of evaluation accuracy of different algorithms (%).
GroupingMean clustering algorithmExpert system algorithmDecision tree algorithmSkill quality88.5885.8695.32Ability and quality85.4280.4595.58Physical quality83.3978.4990.19Knowledge quality80.1270.3590.27Character quality80.6575.6990.35Table1 shows the evaluation accuracy results under different algorithms. Through comparative analysis, it is found that the evaluation results of the expert system are unstable, the accuracy of mean clustering algorithm has been significantly improved, and the error rate of human resource evaluation has also been reduced. The evaluation accuracy of the decision tree algorithm reaches 92%. Compared with several different algorithms, it is found that the decision tree algorithm not only greatly improves the evaluation accuracy but also obtains satisfactory evaluation results. It has obvious advantages, improves the data accuracy in various fields, and promotes the development of the enterprise.In order to better compare the evaluation accuracy results of different algorithms, the following Figure4 is drawn according to the data in the above table to facilitate intuitive understanding of the data.Figure 4
Visualization of evaluation accuracy of different algorithms.As shown in Figure4, in the visualization of the evaluation accuracy of different algorithms, it is obvious that the decision tree algorithm is superior to the other two algorithms, which shows that the use of decision tree is conducive to the management and control of human resources, so as to mobilize the enthusiasm of employees to the greatest extent and, finally, promote the enterprise to achieve its strategic objectives.


### 4.2. Time Efficiency Analysis of Different Algorithms on Enterprise HRM Effectiveness Evaluation

In the enterprise HRM effectiveness evaluation, the correlation analysis is carried out according to the problems, and the data are screened layer by layer to obtain more scientific and satisfactory results. According to the complexity of the data, different algorithms are used to count the enterprise HRM effectiveness evaluation time. Now, the mean clustering algorithm is adopted for the ability quality, skill quality, physical quality, knowledge quality, and character quality, compared with the time efficiency of the expert system algorithm and decision tree algorithm in enterprise HRM effectiveness evaluation, as shown in Table2.Table 2
Comparison of evaluation duration and efficiency of different algorithms.
GroupingMean clustering algorithmExpert system algorithmDecision tree algorithmSkill quality29.4518.9811.11Ability and quality32.4525.6511.98Physical quality30.8920.5810.78Knowledge quality32.3419.7213.13Character quality26.3520.6411.32Table2 shows the evaluation time and efficiency results of different algorithms. Through comparative analysis, it is found that the evaluation time of the expert system is longer, the work efficiency of the mean clustering algorithm is significantly improved, the evaluation time complexity of the decision tree algorithm is small, the work efficiency is greatly improved, and the mined data will be processed objectively.In order to better compare the evaluation duration and efficiency results of different algorithms, the following Figure5 is drawn according to the data in the above table to facilitate intuitive understanding of the data.Figure 5
Visualization of evaluation duration and efficiency of different algorithms.As shown in Figure5, in the visualization diagram of evaluation duration efficiency of different algorithms, it can be clearly seen that the decision tree algorithm and the other two algorithms have opened a significant gap. This directly shows that the use of the decision tree helps to improve the efficiency of human resources and helps enterprises make better decisions.


## 4.1. Analysis on the Accuracy of Different Algorithms for Enterprise HRM Effectiveness Evaluation

With the gradual maturity of data mining technology, the decision tree is widely used in enterprise HRM effectiveness evaluation. As one of the important algorithms in the field of data mining, the algorithm has three important components in intelligent decision-making, including feature, algorithm, and decision-making. When facing problems, the decision tree can deal with the problems of missing data and many fields very steadily. Compared with other algorithms, the decision tree is easier to understand, simple, and intuitive to analyze. Now, aiming at the ability quality, skill quality, physical quality, knowledge quality, and character quality, the mean clustering algorithm, expert system algorithm, and decision tree algorithm are used to compare the accuracy of enterprise HRM efficiency evaluation, as shown in Table1.Table 1
Comparison of evaluation accuracy of different algorithms (%).
GroupingMean clustering algorithmExpert system algorithmDecision tree algorithmSkill quality88.5885.8695.32Ability and quality85.4280.4595.58Physical quality83.3978.4990.19Knowledge quality80.1270.3590.27Character quality80.6575.6990.35Table1 shows the evaluation accuracy results under different algorithms. Through comparative analysis, it is found that the evaluation results of the expert system are unstable, the accuracy of mean clustering algorithm has been significantly improved, and the error rate of human resource evaluation has also been reduced. The evaluation accuracy of the decision tree algorithm reaches 92%. Compared with several different algorithms, it is found that the decision tree algorithm not only greatly improves the evaluation accuracy but also obtains satisfactory evaluation results. It has obvious advantages, improves the data accuracy in various fields, and promotes the development of the enterprise.In order to better compare the evaluation accuracy results of different algorithms, the following Figure4 is drawn according to the data in the above table to facilitate intuitive understanding of the data.Figure 4
Visualization of evaluation accuracy of different algorithms.As shown in Figure4, in the visualization of the evaluation accuracy of different algorithms, it is obvious that the decision tree algorithm is superior to the other two algorithms, which shows that the use of decision tree is conducive to the management and control of human resources, so as to mobilize the enthusiasm of employees to the greatest extent and, finally, promote the enterprise to achieve its strategic objectives.


## 4.2. Time Efficiency Analysis of Different Algorithms on Enterprise HRM Effectiveness Evaluation

In the enterprise HRM effectiveness evaluation, the correlation analysis is carried out according to the problems, and the data are screened layer by layer to obtain more scientific and satisfactory results. According to the complexity of the data, different algorithms are used to count the enterprise HRM effectiveness evaluation time. Now, the mean clustering algorithm is adopted for the ability quality, skill quality, physical quality, knowledge quality, and character quality, compared with the time efficiency of the expert system algorithm and decision tree algorithm in enterprise HRM effectiveness evaluation, as shown in Table2.Table 2
Comparison of evaluation duration and efficiency of different algorithms.
GroupingMean clustering algorithmExpert system algorithmDecision tree algorithmSkill quality29.4518.9811.11Ability and quality32.4525.6511.98Physical quality30.8920.5810.78Knowledge quality32.3419.7213.13Character quality26.3520.6411.32Table2 shows the evaluation time and efficiency results of different algorithms. Through comparative analysis, it is found that the evaluation time of the expert system is longer, the work efficiency of the mean clustering algorithm is significantly improved, the evaluation time complexity of the decision tree algorithm is small, the work efficiency is greatly improved, and the mined data will be processed objectively.In order to better compare the evaluation duration and efficiency results of different algorithms, the following Figure5 is drawn according to the data in the above table to facilitate intuitive understanding of the data.Figure 5
Visualization of evaluation duration and efficiency of different algorithms.As shown in Figure5, in the visualization diagram of evaluation duration efficiency of different algorithms, it can be clearly seen that the decision tree algorithm and the other two algorithms have opened a significant gap. This directly shows that the use of the decision tree helps to improve the efficiency of human resources and helps enterprises make better decisions.


## 5. Discussion

The product of economic development has promoted the birth of enterprises and enhanced the vitality of economic development. In recent years, with the continuous improvement of China’s market economy and the gradual increase of enterprises, China has played a leading role in the process of economic construction. With the progress of the times, the introduction and management of talents, the development of the enterprise itself, and the achievement of social benefits have been strengthened in the process of enterprise development. A management efficiency evaluation system has been established in the management of human resources, and the decision tree algorithm is used to promote the efficiency of talent management, so that the enterprise can operate better and obtain relevant benefits. In the research of Wang et al., the decision tree algorithm was used to analyze and mine information system data to improve efficiency [9]. With the continuous upgrading of information technology, the decision tree algorithm not only plays an important role in enterprises but also is fully utilized in medicine. For diabetes and kidney disease, Chen et al. studied and analyzed the decision tree algorithm in their research, so as to further understand the diagnostic role of the disease [10]. The birth of enterprises has improved the employment rate, but there are also many “leather bag companies” (zombie enterprises). Aiming at the problem of how to accurately identify zombie enterprises, Wu et al. proposed an enterprise identification method of decision tree logical regression, so as to solve the problem of zombie enterprises and provide effectiveness and stability for stabilizing the market order [11]. Economy and technology are now globalized. Trade liberalization is the main factor causing modern enterprise competition. Enterprises must strengthen performance management, and performance management and human resource management efficiency are inseparable. Therefore, human resources are the source of enterprise competition and directly affect the development and survival of enterprises. In the information age and the era of continuous emergence of data, with regard to how to use data to optimize the company’s service scheme, Wang and Chen, in their research report, introduced the decision tree algorithm to analyze the user data and improve the user experience and satisfaction, so as to deal with today’s highly competitive operation environment [12]. Wu analyzed and demonstrated the decision tree algorithm used by enterprises in venture capital and compared the changes of algorithm accuracy before and after extracting principal components [13]. The brain drain in logistics enterprises is relatively serious. Therefore, Yang and Li are using the decision tree algorithm to discuss and apply the brain drain in logistics enterprises, find out the factors affecting employee turnover, and provide a theoretical basis for the company’s human resource management and talent retention in the future [14].In today’s society, the decision tree algorithm is conducive to understanding and analyzing data in enterprise management and accurately solving the problems of the human resource management system. Good decision-making methods can promote the development of enterprises, improve the competitiveness of enterprises, and adapt to the changes of external environment, so that enterprises can obtain good economic benefits and make more contributions to society. In the decision tree algorithm, the enterprise talent management system can make better use of talents, make talents give full play to their strengths, lay a certain foundation for the development of enterprises, better promote China’s economic development, and have a significant impact on China’s international community. Let international friends pay attention to China’s economy and scientific research and development technology and update their understanding of China’s economic development.


## 6. Summary

In the era of database and information network, the data of enterprises increases sharply. The purpose of using the data mining decision tree is to make better work decisions and improve work efficiency. This study studies the enterprise HRM efficiency evaluation index system based on the decision tree algorithm. Through the comparative analysis of mean clustering algorithm, expert system algorithm, and decision tree algorithm, it is concluded that the decision tree can make decisions faster and more accurately in the enterprise HRM efficiency evaluation. The decision tree algorithm can mine relevant invisible information when processing data, improve the usefulness of data, improve the current operation and management efficiency of enterprises, and better promote the effective development of enterprise HRM efficiency evaluation.



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