Research on Human Resource Management Performance Evaluation Method Based on Chaos Optimization Algorithm

Abstract

All enterprise components and business activities are centered on the customer. Whether human resource management is reasonable and effective not only is a matter of human resource management, but also directly influences whether other resources can be used reasonably and effectively, and determines the efficiency of business operations. In human resource management, perfect performance evaluation criteria are developed based on the actual situation in order to evaluate employee productivity and work ethic. Nonetheless, in the process of actual enterprise HR management performance evaluation work, there are not only imperfect performance evaluation standards, but also relatively objective evaluation standards, and HR cannot conduct a comprehensive analysis of each position, which has a negative impact on the quality of enterprise HR management work. This paper improves the performance evaluation model for the original data mining and proposes a human resource management performance evaluation method based on chaotic optimization algorithm, which generates the initial values of evaluation data through chaotic logistic mapping, bringing the initial data closer to the optimal value, reducing the impact of random initialization on the algorithm’s performance, and when the model is trained, it can make the algorithm’s performance more stable. Capability thereby addresses the flaws in the original evaluation method. Experiments demonstrate that our method significantly improves the precision of model training and prediction.