Application of an Improved Clustering Algorithm of Neural Networks in Performance Appraisal Systems

Abstract

With the development of economic globalization, human resource competition has long become the key core of enterprise development and peer competition. Reasonably Formulating an enterprise’s employee performance appraisal management system and conducting standardized, fair, and just appraisal management are the basic requirements for the survival and development of an enterprise. This paper studies the application of an improved clustering algorithm based on neural network in an employee performance appraisal management system and explores its application value in the employee performance appraisal management system by using the improved ART2 clustering method that draws on leakage competition and Hebb rules. The experimental results of this paper show that the satisfaction of this system in the four aspects of integrated data management, system stability and convenience, and transparency in performance appraisal are all above 66%. This shows that this system has superior performance and good reference value.