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

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.