# Evaluation Algorithm of Labor Legal Effectiveness for Affirmative Action against Gender Discrimination

Authors: Liao Juan
Journal: Journal of Mathematics
 (2022)

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## Abstract

Aiming at the problems of large evaluation error and low accuracy of determining the key degree of evaluation indicators in the existing evaluation of labor legal effectiveness, this paper designs a labor legal effectiveness evaluation algorithm for affirmative action against gender discrimination. Firstly, using hits degree, the degree of gender discrimination, and social influence, enterprise practice and government supervision and management are determined as the evaluation indexes of labor legal effectiveness in this paper, and on this basis, the labor legal effectiveness evaluation system against gender discrimination is designed. Then, the judgment matrix of the evaluation index of labor legal effectiveness against gender discrimination is constructed. After normalization, the weight of the evaluation index is calculated by entropy method, which lays a foundation for subsequent research. Finally, the tree enhanced Bayesian network is used to classify the labor legal effectiveness evaluation indicators, and the correlation between the indicators is determined through the Spearman rank correlation coefficient. Finally, the labor legal effectiveness evaluation model against gender discrimination is designed through the clustering algorithm, and the labor legal effectiveness evaluation indicators against gender discrimination are input to complete the effective evaluation. The experimental results show that the error of the evaluation algorithm is small, and the accuracy of determining the key degree of the evaluation index is high.


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## Body

## 1. Introduction

Throughout history, human society has been plagued by racial, ethnic, and gender discrimination for a long time. Discrimination exists in many areas of social life in different forms, such as gender discrimination, employment discrimination, geographical discrimination, health discrimination, age discrimination, registered residence discrimination, academic discrimination, facial discrimination, religious discrimination, ethnic discrimination, and discrimination in the country [1].At present, China’s domestic academic circles generally believe that gender discrimination is an exclusive unfair treatment based on gender. However, there is no final conclusion on how to define gender discrimination. There are several important understandings: not all gender inequalities are equal to gender discrimination. Differential treatment based on the internal needs of occupation or the special needs of work post and the needs to maintain national security and public order do not belong to the scope of gender discrimination [2]. Gender discrimination is not only discrimination against women but also discrimination against men and discrimination against cross gender groups. Gender politics is essentially the negation and deprivation of women’s basic human rights and freedoms. Antigender discrimination has important significance to protect women’s human rights. Gender discrimination in employment is a very common problem faced by our society at present. Men and women have different value recognition in the labor market based on gender differences. There is a serious gender stratification in the labor market, the economic income gap between men and women continues to expand, and the vulnerable tendency of women’s overall status is also emerging. However, gender differences do not constitute a legitimate reason for gender differential treatment [3]. At present, the important reason why women generally suffer from social discrimination lies in the unfair social division of labor mode of “men outside and women inside,” as well as the social reality that reproductive responsibility is mainly borne by women. The reality requires us to formulate relevant antidiscrimination laws and regulations as soon as possible, recognize the existence of discrimination, deeply understand the harm caused by discrimination to gender equality, promote the development of antigender discrimination work by clearly defining the constituent elements of discrimination and strengthening legislative and judicial practice, accelerate legal reform and innovation, and establish a sound system for the protection of women’s rights, change and enhance social values, affirm the social value of women’s housework, and establish a sound and perfect maternity security system, so as to change women’s disadvantage and development situation caused by childbirth and promote gender equality [4].Gender equality is a human rights issue, and the essence of gender inequality is the inequality of rights [5]. The pursuit of gender equality is an important part of a harmonious society. Building a harmonious world requires strengthening the protection of human rights, especially the vulnerable groups. In recent years, great progress has been made in the legislative work related to the protection of women’s rights and antigender discrimination in China, which has been formed on the basis of the constitution of the People’s Republic of China and the law of the People’s Republic of China on the protection of women’s rights and interests. China’s constitution stipulates the fundamental principle of equality before the law and defines that women enjoy equal rights with men in political, economic, cultural, social and family life equal rights [6]. At the level of domestic legal system, China has strengthened relevant legislation against gender discrimination in many social fields such as labor, family life, education, and political participation and is committed to promoting gender equality. The Chinese people’s law on the protection of persons with disabilities was adopted in 1990 and revised in 2008 It stipulates that discrimination on the basis of disability and the use of the media or other means to insult, infringe, or belittle the personality of persons with disabilities are prohibited: the labor law was promulgated in 1994, which stipulates that workers shall not be subject to any discrimination in employment on the grounds of nationality, race, gender, and religious belief: in 1995, the government issued the first program of action to promote gender equality. The outline for the development of Chinese women (1995–2000) followed closely with the outline for the development of Chinese women (2001–2010), the second national program of action to promote gender equality in 2001. It has put forward 34 main objectives and 100 strategic measures in six areas: women and the economy, women’s participation in decision-making and management, women and education, women and health, women and law, and women and the environment. The marriage law in 2001 explicitly included the prohibition of domestic violence for the first time: the law on the protection of women’s rights and interests was newly revised in 2005, emphasizes that women and men have equal rights in politics, economy, culture, and education, gives full play to women’s role in socialist modernization, prohibits discrimination, abuse, abandonment, and mutilation of women, and promotes gender equality: the employment promotion law was formulated on the basis of the labor law in 2007; it further clearly requires employers to uniformly provide equal employment opportunities and fair employment conditions to workers and prohibit refusing to hire women on the basis of gender, improving women’s employment standards, or setting other discriminatory restrictions.After the promulgation of these legal provisions, the evaluation of their effectiveness is the key to measure the role of legal provisions. Therefore, this paper designs an evaluation algorithm of labor legal effect.


## 2. Design of Labor Legal Effectiveness Evaluation Algorithm for Affirmative Action against Gender Discrimination

In this section, we determine the evaluation index of labor legal effectiveness and construct the system against gender discrimination. Next, the weight of labor legal effectiveness evaluation index against gender discrimination is calculated. Then, we also explain the design of the proposed algorithm against gender discrimination.


### 2.1. Determination of Labor Legal Effectiveness Evaluation Index and System Construction against Gender Discrimination

In order to realize the design of labor legal effectiveness evaluation algorithm in this paper, firstly, we determine the labor legal effectiveness evaluation index against gender discrimination and build an effective evaluation system according to the determined labor legal effectiveness evaluation index against gender discrimination. In this part, according to the existing research, the evaluation indicators of labor legal effectiveness are sorted out, and the four key indicators that can best reflect the legal effectiveness are selected on the basis of advantages and disadvantages of the evaluation indicators, so as to complete the determination of the evaluation indicators of labor legal effectiveness. Among the selected legal effectiveness indicators, the degree of gender discrimination, social influence, enterprise practice, and government supervision and management are determined as the evaluation indicators of industrial legal effectiveness in this paper.According to the determined legal effectiveness indicators, the authority of these indicators is determined by HITS algorithm to verify that the indicators determined by this method comply with the current evaluation of legal efficiency [7]. The criticality of evaluation indicators is set through HITS algorithm, and the most important indicators are determined among these indicators. These indicators are designed as a root set R, which includes a high proportion of authority assurance. The set is expanded to calculate the authority of new evaluation indicators through continuous iteration [8]. Assume that the indicator set in this set is(1)T=t1,t2,t3,…,tn.Each index of authority isan, and a higher key index is bn:(2)an=∑i=1mbii∈bnm.In (2), bnm represents the key degree value of the rating index.(3)bn=∑i=1nCii∈Rnm.In (3), Rn represents the yield values in the rating index set.According to the above calculation, the authority degree of legal efficiency of the above four indicators is determined. Based on the four key evaluation indexes, a multilevel evaluation index system is constructed, as shown in Figure1.Figure 1
Schematic diagram of labor legal effectiveness evaluation system against gender discrimination.In the key process of calculating the evaluation index by the algorithm, in the determination of the evaluation index of labor legal effectiveness against gender discrimination and the construction of the system, the hits degree is used to determine the degree of gender discrimination, social influence, enterprise practice, and government supervision and management as the evaluation index of labor legal effectiveness studied in this paper. The evaluation system of labor legal effect against gender discrimination is designed.


### 2.2. Weight Calculation of Labor Legal Effectiveness Evaluation Index against Gender Discrimination

In the abovementioned evaluation indicators of labor legal effectiveness against gender discrimination and the evaluation process constructed, each indicator plays a different role and influence. In order to ensure the scientificity of the evaluation [6], different weights will be given to the unused indicators. The entropy weight method is used to determine the index weight. The judgment matrix of the evaluation index is defined as r. After normalization, the relative membership function formula of the index is obtained:(4)kij=rij−rminrmax−rmin.In (4), rij is the term in the matrix and rmin, rmax are the minimum and maximum index values under the same index, respectively.The entropy of the evaluation index is calculated by the definition of entropy, and the calculation formula can be expressed as(5)Hi=−1Inm∑j=1mfijInfij,where fij represents the ratio of single indicator membership in all index membership. The entropy weight of the i index can be calculated as(6)ωi=1−Hin−∑i=1nHi,Replacing the calculation results in (5) into formula (6) can calculate the entropy right calculation results of all evaluation indicators, and the sum of all index weights is equal to 1. Replacing the calculation results in (5) into formula (6) can calculate the entropy right calculation results of all evaluation indicators, and the sum of all index weights is 1.In the weight calculation of labor legal effectiveness evaluation indicators against gender discrimination, the evaluation matrix of labor legal effectiveness evaluation indicators against gender discrimination is constructed. After normalization, the weight [9] of the evaluation indicators is calculated by the entropy method to lay the foundation for subsequent research.


### 2.3. Design of Evaluation Algorithm of Labor Legal Effect against Gender Discrimination

Based on the weight of the above evaluation index of labor legal effectiveness against gender discrimination, an evaluation algorithm of labor legal effectiveness against gender discrimination is designed. In this paper, Bayesian algorithm is used to design the labor legal effectiveness evaluation algorithm against gender discrimination. Naive Bayesian is a simple technique for constructing classifiers models that assign class labels to problem instances. In this, there is not a single algorithm but a family of algorithms based on a common principle. A naive traditional Bayesian network has great advantages in dealing with complex system state problems, but the assumption of traditional Bayesian network also brings some limitations to its application. The main reason is that Bayesian network is a NP hard problem. When there are many attributes of the research object, the calculation problem of Bayesian network without any learning constraints will become very huge. In order to improve the method, many experts and scholars are studying the Bayesian network method. Friedman transformed the Bayesian network into a tree enhanced naive Bayesian classifier (abbreviated as tree enhanced naive Bayesian) [10]. The tree enhanced Bayesian method is an improvement of the naive Bayesian network method. Compared with the naive Bayesian network, the tree enhanced Bayesian method has a certain relaxation in the requirements of assumptions, which can combine the advantages of the simplicity of naive Bayesian and the ability of Bayesian network to simply and clearly express the dependence between variables. Then, it can improve the classification ability of the method. Tree enhanced naive Bayes is an excellent classification model with good comprehensive performance. It balances the accuracy and learning efficiency in the classification process. The classification performance based on tree enhanced Bayesian network is also significantly higher than that based on traditional naive Bayes [11].The constrained Bayesian network structure adopted in the labor legal effectiveness evaluation is(7)W=x1,x2,x3,…,xn,f.In formula (7), x represents a discrete set of attribute variables, and C represents class variables. There is no parent node for this variable. The class variable is the parent of all the other attribute value variables x, which has at most one attribute variable. That is, any attribute variable other than the class variable C has two parents, and one of the parents is the class variable [12] C; assuming that ∏xi represents the parent of an attribute variable xi, it can be obtained:(8)∏f=α,f∈∏xi.A naive Bayesian model graph can be enhanced for a basic tree according to the description above, as shown in Figure2.Figure 2
Basic tree enhanced naive Bayesian model.Compared with Bayesian network model, tree enhanced naive Bayesian model can only have two parent nodes at most for each node, except the inner node, of which only one parent node can be a nonclass variable, and the class variable must be the parent node of other variables. This is mainly to reduce the search space and solve the problem of “state explosion” to a certain extent. In Bayesian network, the conditional probability table will increase with the increase of the number of parent nodes, which may become a “state explosion” problem. Limiting the number of parent nodes to less than 2 can effectively solve the “state explosion” problem caused by the large number of parent nodes [13].For any unknown category, the evaluation index examples that need to be classified are(9)vi=<vi1,vi2,vi3,…,vin>.The legal effectiveness indexes to be evaluated are classified by tree enhanced Bayesian method. If the largest class label is determined according to the formula calculated by Yebes, there is(10)p=argmaxpvi1,vi2,vi3,…,vinppci.Among them, the evaluation set can be deduced according to the structure of tree enhanced naive Bayes.Based on the classification of labor legal effectiveness evaluation indexes according to the tree enhanced Bayesian algorithm, the correlation of each evaluation index is analyzed to realize the design of the algorithm. Correlation analysis is to measure the correlation of two or more variables. In the research of microblog influence evaluation algorithm, the correlation coefficient in statistical method is often used to measure the correlation and difference of the algorithm. This paper selects Spearman rank correlation coefficient to measure the correlation between crank algorithm and existing microblog influence algorithm [14].Spearman’s rank correlation coefficient is an index to measure the dependence between two parameters, which was proposed by Charles Spearman. The biggest feature of Spearman’s rank correlation coefficient is that the correlation can be calculated regardless of the distribution of parameters and the size of samples. The calculation formula is as follows: assuming that there are data setsX and Y, then(11)E=∑ixi−x¯yi−y¯xi−x¯yi−y¯2.In (11), xi represents the key degree ranking of x in the evaluation index set, and yi represents the y secondary degree ranking of u in the data set, the x¯ represents the mean of the critical degree in the set of evaluation metrics, and the y¯ represents the secondary degree in the data set. To simplify the computational simplicity of the evaluation index phase, the formula is reduced to(12)E′=2∑idinn−1.In (12), di represents the range of values. When the larger the value represents the greater the degree of association between the evaluation index, the correlation of the evaluation index reflects the simplicity of the evaluation.Based on the determination of the correlation of the above evaluation indexes, this paper designs an evaluation model of labor legal effectiveness against gender discrimination with the help of clustering algorithm and inputs the determined evaluation indexes with high correlation into it to achieve the final effective evaluation. The main principle of K-means clustering algorithm is as follows: randomly selectk points as the particles of the initial clustering, then calculate the distance from each data point to the k particles, collect the data point to the nearest particle, traverse all data objects, and get the k categories of the first clustering. Calculate the new class center according to the last clustering result. If the distance between the class center and the class center of the last clustering is small, it indicates that the algorithm has converged. Otherwise, recalculate the distance from each data point to the new k particles and classify each point into a new particle class and continue the iteration. When the distance between all new particles and the last particle is less than a given initial value, it means that all particle classes have converged and a local optimal solution is obtained, then the iteration is stopped.In a given set of evaluation metrics, each setei represents an evaluation indicator element, where each element includes the attributes of multiple evaluation metrics. Fuzzy clustering is to determine the evaluation index as class C, setting(13)U=eiu1,u2,u3,…,un.The formula represents the centroid distance of the evaluation index. In the fuzzy division, each evaluation index cannot be rigidly divided or belong to a class. It needs to be classified according to the evaluation membership degree. The unified results obtained at this time are(14)Gmei=∑i=1n∑cfuimdi,where Gm represents the centroid of mass distance between the data after unification and ui indicates the fuzzy index.Based on this basis, we design the labor legal effectiveness evaluation model against gender discrimination. First, initialize the category of the evaluation index and set the fuzzy number toM, and the calculation of the iterative stop threshold, when the maximum number of iterations is L and the fuzzy partition matrix is U, is obtained:(15)HijI+1=1∑k=1cdij/dkj/n.Then, the results of this model evaluation are updated and the results are(16)h=1∑i=1cHij/dij/dkj.In (16), the h output is the final effectiveness evaluation.Based on the above analysis, the design of labor legal effectiveness evaluation algorithm against gender discrimination is realized. The overall evaluation process is shown in Figure3.Figure 3
Design process of labor legal effectiveness evaluation algorithm against gender discrimination.Figure3 explains the evaluation algorithm of labor legal effectiveness. Initialize evaluation index and calculate the index weight through fuzzy unified index membership. If the clustering model of evaluation has been successfully constructed, it gives the results and stops the process. If clustering model has not been constructed successfully, repeat the process again to get the effective output. In the design of labor legal effectiveness evaluation algorithm against gender discrimination, firstly, the tree is used to enhance the Bayesian network to classify the labor legal effectiveness evaluation indicators, and the correlation between the indicators is determined by Spearman’s rank correlation coefficient. Finally, the labor legal effectiveness evaluation model against gender discrimination is designed by clustering algorithm. Input the evaluation index of labor legal effectiveness against gender discrimination into it to complete the effective evaluation.


## 2.1. Determination of Labor Legal Effectiveness Evaluation Index and System Construction against Gender Discrimination

In order to realize the design of labor legal effectiveness evaluation algorithm in this paper, firstly, we determine the labor legal effectiveness evaluation index against gender discrimination and build an effective evaluation system according to the determined labor legal effectiveness evaluation index against gender discrimination. In this part, according to the existing research, the evaluation indicators of labor legal effectiveness are sorted out, and the four key indicators that can best reflect the legal effectiveness are selected on the basis of advantages and disadvantages of the evaluation indicators, so as to complete the determination of the evaluation indicators of labor legal effectiveness. Among the selected legal effectiveness indicators, the degree of gender discrimination, social influence, enterprise practice, and government supervision and management are determined as the evaluation indicators of industrial legal effectiveness in this paper.According to the determined legal effectiveness indicators, the authority of these indicators is determined by HITS algorithm to verify that the indicators determined by this method comply with the current evaluation of legal efficiency [7]. The criticality of evaluation indicators is set through HITS algorithm, and the most important indicators are determined among these indicators. These indicators are designed as a root set R, which includes a high proportion of authority assurance. The set is expanded to calculate the authority of new evaluation indicators through continuous iteration [8]. Assume that the indicator set in this set is(1)T=t1,t2,t3,…,tn.Each index of authority isan, and a higher key index is bn:(2)an=∑i=1mbii∈bnm.In (2), bnm represents the key degree value of the rating index.(3)bn=∑i=1nCii∈Rnm.In (3), Rn represents the yield values in the rating index set.According to the above calculation, the authority degree of legal efficiency of the above four indicators is determined. Based on the four key evaluation indexes, a multilevel evaluation index system is constructed, as shown in Figure1.Figure 1
Schematic diagram of labor legal effectiveness evaluation system against gender discrimination.In the key process of calculating the evaluation index by the algorithm, in the determination of the evaluation index of labor legal effectiveness against gender discrimination and the construction of the system, the hits degree is used to determine the degree of gender discrimination, social influence, enterprise practice, and government supervision and management as the evaluation index of labor legal effectiveness studied in this paper. The evaluation system of labor legal effect against gender discrimination is designed.


## 2.2. Weight Calculation of Labor Legal Effectiveness Evaluation Index against Gender Discrimination

In the abovementioned evaluation indicators of labor legal effectiveness against gender discrimination and the evaluation process constructed, each indicator plays a different role and influence. In order to ensure the scientificity of the evaluation [6], different weights will be given to the unused indicators. The entropy weight method is used to determine the index weight. The judgment matrix of the evaluation index is defined as r. After normalization, the relative membership function formula of the index is obtained:(4)kij=rij−rminrmax−rmin.In (4), rij is the term in the matrix and rmin, rmax are the minimum and maximum index values under the same index, respectively.The entropy of the evaluation index is calculated by the definition of entropy, and the calculation formula can be expressed as(5)Hi=−1Inm∑j=1mfijInfij,where fij represents the ratio of single indicator membership in all index membership. The entropy weight of the i index can be calculated as(6)ωi=1−Hin−∑i=1nHi,Replacing the calculation results in (5) into formula (6) can calculate the entropy right calculation results of all evaluation indicators, and the sum of all index weights is equal to 1. Replacing the calculation results in (5) into formula (6) can calculate the entropy right calculation results of all evaluation indicators, and the sum of all index weights is 1.In the weight calculation of labor legal effectiveness evaluation indicators against gender discrimination, the evaluation matrix of labor legal effectiveness evaluation indicators against gender discrimination is constructed. After normalization, the weight [9] of the evaluation indicators is calculated by the entropy method to lay the foundation for subsequent research.


## 2.3. Design of Evaluation Algorithm of Labor Legal Effect against Gender Discrimination

Based on the weight of the above evaluation index of labor legal effectiveness against gender discrimination, an evaluation algorithm of labor legal effectiveness against gender discrimination is designed. In this paper, Bayesian algorithm is used to design the labor legal effectiveness evaluation algorithm against gender discrimination. Naive Bayesian is a simple technique for constructing classifiers models that assign class labels to problem instances. In this, there is not a single algorithm but a family of algorithms based on a common principle. A naive traditional Bayesian network has great advantages in dealing with complex system state problems, but the assumption of traditional Bayesian network also brings some limitations to its application. The main reason is that Bayesian network is a NP hard problem. When there are many attributes of the research object, the calculation problem of Bayesian network without any learning constraints will become very huge. In order to improve the method, many experts and scholars are studying the Bayesian network method. Friedman transformed the Bayesian network into a tree enhanced naive Bayesian classifier (abbreviated as tree enhanced naive Bayesian) [10]. The tree enhanced Bayesian method is an improvement of the naive Bayesian network method. Compared with the naive Bayesian network, the tree enhanced Bayesian method has a certain relaxation in the requirements of assumptions, which can combine the advantages of the simplicity of naive Bayesian and the ability of Bayesian network to simply and clearly express the dependence between variables. Then, it can improve the classification ability of the method. Tree enhanced naive Bayes is an excellent classification model with good comprehensive performance. It balances the accuracy and learning efficiency in the classification process. The classification performance based on tree enhanced Bayesian network is also significantly higher than that based on traditional naive Bayes [11].The constrained Bayesian network structure adopted in the labor legal effectiveness evaluation is(7)W=x1,x2,x3,…,xn,f.In formula (7), x represents a discrete set of attribute variables, and C represents class variables. There is no parent node for this variable. The class variable is the parent of all the other attribute value variables x, which has at most one attribute variable. That is, any attribute variable other than the class variable C has two parents, and one of the parents is the class variable [12] C; assuming that ∏xi represents the parent of an attribute variable xi, it can be obtained:(8)∏f=α,f∈∏xi.A naive Bayesian model graph can be enhanced for a basic tree according to the description above, as shown in Figure2.Figure 2
Basic tree enhanced naive Bayesian model.Compared with Bayesian network model, tree enhanced naive Bayesian model can only have two parent nodes at most for each node, except the inner node, of which only one parent node can be a nonclass variable, and the class variable must be the parent node of other variables. This is mainly to reduce the search space and solve the problem of “state explosion” to a certain extent. In Bayesian network, the conditional probability table will increase with the increase of the number of parent nodes, which may become a “state explosion” problem. Limiting the number of parent nodes to less than 2 can effectively solve the “state explosion” problem caused by the large number of parent nodes [13].For any unknown category, the evaluation index examples that need to be classified are(9)vi=<vi1,vi2,vi3,…,vin>.The legal effectiveness indexes to be evaluated are classified by tree enhanced Bayesian method. If the largest class label is determined according to the formula calculated by Yebes, there is(10)p=argmaxpvi1,vi2,vi3,…,vinppci.Among them, the evaluation set can be deduced according to the structure of tree enhanced naive Bayes.Based on the classification of labor legal effectiveness evaluation indexes according to the tree enhanced Bayesian algorithm, the correlation of each evaluation index is analyzed to realize the design of the algorithm. Correlation analysis is to measure the correlation of two or more variables. In the research of microblog influence evaluation algorithm, the correlation coefficient in statistical method is often used to measure the correlation and difference of the algorithm. This paper selects Spearman rank correlation coefficient to measure the correlation between crank algorithm and existing microblog influence algorithm [14].Spearman’s rank correlation coefficient is an index to measure the dependence between two parameters, which was proposed by Charles Spearman. The biggest feature of Spearman’s rank correlation coefficient is that the correlation can be calculated regardless of the distribution of parameters and the size of samples. The calculation formula is as follows: assuming that there are data setsX and Y, then(11)E=∑ixi−x¯yi−y¯xi−x¯yi−y¯2.In (11), xi represents the key degree ranking of x in the evaluation index set, and yi represents the y secondary degree ranking of u in the data set, the x¯ represents the mean of the critical degree in the set of evaluation metrics, and the y¯ represents the secondary degree in the data set. To simplify the computational simplicity of the evaluation index phase, the formula is reduced to(12)E′=2∑idinn−1.In (12), di represents the range of values. When the larger the value represents the greater the degree of association between the evaluation index, the correlation of the evaluation index reflects the simplicity of the evaluation.Based on the determination of the correlation of the above evaluation indexes, this paper designs an evaluation model of labor legal effectiveness against gender discrimination with the help of clustering algorithm and inputs the determined evaluation indexes with high correlation into it to achieve the final effective evaluation. The main principle of K-means clustering algorithm is as follows: randomly selectk points as the particles of the initial clustering, then calculate the distance from each data point to the k particles, collect the data point to the nearest particle, traverse all data objects, and get the k categories of the first clustering. Calculate the new class center according to the last clustering result. If the distance between the class center and the class center of the last clustering is small, it indicates that the algorithm has converged. Otherwise, recalculate the distance from each data point to the new k particles and classify each point into a new particle class and continue the iteration. When the distance between all new particles and the last particle is less than a given initial value, it means that all particle classes have converged and a local optimal solution is obtained, then the iteration is stopped.In a given set of evaluation metrics, each setei represents an evaluation indicator element, where each element includes the attributes of multiple evaluation metrics. Fuzzy clustering is to determine the evaluation index as class C, setting(13)U=eiu1,u2,u3,…,un.The formula represents the centroid distance of the evaluation index. In the fuzzy division, each evaluation index cannot be rigidly divided or belong to a class. It needs to be classified according to the evaluation membership degree. The unified results obtained at this time are(14)Gmei=∑i=1n∑cfuimdi,where Gm represents the centroid of mass distance between the data after unification and ui indicates the fuzzy index.Based on this basis, we design the labor legal effectiveness evaluation model against gender discrimination. First, initialize the category of the evaluation index and set the fuzzy number toM, and the calculation of the iterative stop threshold, when the maximum number of iterations is L and the fuzzy partition matrix is U, is obtained:(15)HijI+1=1∑k=1cdij/dkj/n.Then, the results of this model evaluation are updated and the results are(16)h=1∑i=1cHij/dij/dkj.In (16), the h output is the final effectiveness evaluation.Based on the above analysis, the design of labor legal effectiveness evaluation algorithm against gender discrimination is realized. The overall evaluation process is shown in Figure3.Figure 3
Design process of labor legal effectiveness evaluation algorithm against gender discrimination.Figure3 explains the evaluation algorithm of labor legal effectiveness. Initialize evaluation index and calculate the index weight through fuzzy unified index membership. If the clustering model of evaluation has been successfully constructed, it gives the results and stops the process. If clustering model has not been constructed successfully, repeat the process again to get the effective output. In the design of labor legal effectiveness evaluation algorithm against gender discrimination, firstly, the tree is used to enhance the Bayesian network to classify the labor legal effectiveness evaluation indicators, and the correlation between the indicators is determined by Spearman’s rank correlation coefficient. Finally, the labor legal effectiveness evaluation model against gender discrimination is designed by clustering algorithm. Input the evaluation index of labor legal effectiveness against gender discrimination into it to complete the effective evaluation.


## 3. Experimental Analysis

The purpose of this section is to verify the productivity and validation of the proposed work. First, the environment and the conditions are described in which the experiment is performed. Then, experimental index design is talked about. Lastly, the outcomes of the experiment are analyzed and investigated.


### 3.1. Experimental Environment

In order to verify the effectiveness of the algorithm designed in this paper, experimental analysis is carried out. In the experiment, the data of complaints about gender discrimination in enterprise recruitment in 2020 were collected through the network and the amount of data collected was 1000. In the experiment, 1000 pieces of data were collected on a local government website because of insufficient supervision of gender discrimination and through the form of online questionnaire to determine the relevant data of 1000 work-related gender discrimination. The sample training data set is 3000, in which 2000 pieces of positive energy data are selected. In the experiment, the experimental results are integrated by data statistics software. The type of data collected is shown in Figure4.Figure 4
Experimental sample data collection mode.


### 3.2. Experimental Index Design

Based on the experimental sample data set determined above, the experiment compares and analyzes the effectiveness evaluation method based on system simulation big data, the statistical data reliability evaluation method based on robust principal component regression, and the method in this paper. In the experiment, the evaluation error and the accuracy of the key degree of the evaluation index are taken as the indexes.


### 3.3. Analysis of Experimental Results

In order to verify the effectiveness of this method in the evaluation of labor legal effectiveness against gender discrimination based on the sample data, the experiment compares and analyzes the effectiveness evaluation method based on system simulation big data, the statistical data reliability evaluation method based on robust principal component regression, and the evaluation error of this method in the sample data. The results are shown in Figure5.Figure 5
Analysis of evaluation error results of different algorithms.By analyzing the experimental results in Figure5, it can be seen that with the continuous increase of the evaluated sample data, there are some differences in the evaluation errors of the sample data using the efficiency evaluation method based on system simulation big data, the statistical data reliability evaluation method based on robust principal component regression, and the method in this paper. When the sample data size is 500, the error of the evaluation method in this paper is about 2.9%, the evaluation error of the efficiency evaluation method based on system simulation big data is 6.9%, and the evaluation error of the statistical data reliability evaluation method based on robust principal component regression is about 8.9%. When the sample data size is 1000, the error of the evaluation method in this paper is about 1.3%, the evaluation error of the efficiency evaluation method based on system simulation big data is 5.1%, and the evaluation error of the statistical data reliability evaluation method based on robust principal component regression is about 6.1%. From the evaluation result curve, it can be seen that the evaluation error of this method is low, which verifies that this method is feasible.In order to further verify the effectiveness of the proposed method, the effectiveness evaluation method based on system simulation big data, the statistical data reliability evaluation method based on robust principal component regression, and the accuracy of the method in determining the key degree of the evaluation index of sample data are experimentally analyzed. The results are shown in Figure6.Figure 6
Accuracy analysis of key degree determination of indicators in different methods.By analyzing the result data in Figure6, it can be seen that with the increase of the number of samples to determine the accuracy of the evaluation index of sample data, the efficiency evaluation method based on system simulation big data is adopted. There are some differences between the statistical data reliability evaluation method based on robust principal component regression and the method in this paper. Among them, the highest accuracy of using this method to determine the key degree of evaluation indicators is about 98%, while the accuracy of the other two methods is lower than this method although it is within a reasonable range. Therefore, the effectiveness of this method is verified.


## 3.1. Experimental Environment

In order to verify the effectiveness of the algorithm designed in this paper, experimental analysis is carried out. In the experiment, the data of complaints about gender discrimination in enterprise recruitment in 2020 were collected through the network and the amount of data collected was 1000. In the experiment, 1000 pieces of data were collected on a local government website because of insufficient supervision of gender discrimination and through the form of online questionnaire to determine the relevant data of 1000 work-related gender discrimination. The sample training data set is 3000, in which 2000 pieces of positive energy data are selected. In the experiment, the experimental results are integrated by data statistics software. The type of data collected is shown in Figure4.Figure 4
Experimental sample data collection mode.


## 3.2. Experimental Index Design

Based on the experimental sample data set determined above, the experiment compares and analyzes the effectiveness evaluation method based on system simulation big data, the statistical data reliability evaluation method based on robust principal component regression, and the method in this paper. In the experiment, the evaluation error and the accuracy of the key degree of the evaluation index are taken as the indexes.


## 3.3. Analysis of Experimental Results

In order to verify the effectiveness of this method in the evaluation of labor legal effectiveness against gender discrimination based on the sample data, the experiment compares and analyzes the effectiveness evaluation method based on system simulation big data, the statistical data reliability evaluation method based on robust principal component regression, and the evaluation error of this method in the sample data. The results are shown in Figure5.Figure 5
Analysis of evaluation error results of different algorithms.By analyzing the experimental results in Figure5, it can be seen that with the continuous increase of the evaluated sample data, there are some differences in the evaluation errors of the sample data using the efficiency evaluation method based on system simulation big data, the statistical data reliability evaluation method based on robust principal component regression, and the method in this paper. When the sample data size is 500, the error of the evaluation method in this paper is about 2.9%, the evaluation error of the efficiency evaluation method based on system simulation big data is 6.9%, and the evaluation error of the statistical data reliability evaluation method based on robust principal component regression is about 8.9%. When the sample data size is 1000, the error of the evaluation method in this paper is about 1.3%, the evaluation error of the efficiency evaluation method based on system simulation big data is 5.1%, and the evaluation error of the statistical data reliability evaluation method based on robust principal component regression is about 6.1%. From the evaluation result curve, it can be seen that the evaluation error of this method is low, which verifies that this method is feasible.In order to further verify the effectiveness of the proposed method, the effectiveness evaluation method based on system simulation big data, the statistical data reliability evaluation method based on robust principal component regression, and the accuracy of the method in determining the key degree of the evaluation index of sample data are experimentally analyzed. The results are shown in Figure6.Figure 6
Accuracy analysis of key degree determination of indicators in different methods.By analyzing the result data in Figure6, it can be seen that with the increase of the number of samples to determine the accuracy of the evaluation index of sample data, the efficiency evaluation method based on system simulation big data is adopted. There are some differences between the statistical data reliability evaluation method based on robust principal component regression and the method in this paper. Among them, the highest accuracy of using this method to determine the key degree of evaluation indicators is about 98%, while the accuracy of the other two methods is lower than this method although it is within a reasonable range. Therefore, the effectiveness of this method is verified.


## 4. Conclusion

In order to improve the evaluation effect of the existing productiveness legal effectiveness assessment, this paper designed a labor felony effectiveness evaluation algorithm for affirmative action against gender discrimination. The proposed method used hits degree for the determination of the degree of gender discrimination, social influence, enterprise practice, and government supervision and management as the evaluation indexes of labor legal effectiveness in this paper. On this basis, an evaluation system of labor legal effectiveness against gender discrimination was designed, which is composed of constructing the judgment matrix of the evaluation index of labor legal effectiveness against gender discrimination. After normalization, the entropy method was used to calculate the weight of the evaluation index, so as to lay the foundation for subsequent research. The tree enhanced Bayesian network was used to classify the labor legal effectiveness evaluation indexes, and the correlation between the indexes was determined through the Spearman rank correlation coefficient. Finally, the labor legal effectiveness evaluation model against gender discrimination was designed through the clustering algorithm, and the labor legal effectiveness evaluation indexes against gender discrimination were input to complete the effective evaluation. This algorithm gave more advanced and better results. Indexes in this system evaluated more precisely and reduced the chances of uncertainty. The experimental results showed that the error of the evaluation algorithm was small, whereas the accuracy of determining the key degree of the evaluation index was high and accurate.



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*Source: 4073208-2022-01-07.xml*