Construction of Human Resource Management System Based on Machine Learning and Analysis of Influencing Factors

Abstract

In this study, we present an in-depth analysis of the development of human resource management systems that leverage machine learning techniques, as well as the factors that influence their construction. Drawing upon extensive data analysis, our findings illuminate the constructive impact of these systems on enhancing organizational performance. To provide a nuanced understanding, we compare and contrast traditional human resource management practices with contemporary systems that integrate machine learning in recruitment, training, and performance evaluation processes. The results of data analysis show that the system based on machine learning shows higher efficiency and accuracy in many aspects. Specifically, in the recruitment process, the method based on machine learning successfully predicted the matching degree of candidates, and improved the recruitment success rate to 45%, compared with the traditional method, the success rate was 30%; In the training session, the machine learning algorithm makes personalized recommendations according to the learning situation and learning needs of employees, which improves the training effect by 35%, and the improvement rate is 20% compared with the traditional method; In performance evaluation, the objective evaluation method based on machine learning reduces the interference of human factors, improves the fairness and accuracy of evaluation, and the accuracy rate is increased by 50% compared with traditional methods.