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

Introduction

Through relevant background investigations, companies spend a lot of human and material resources on performance appraisals, but their execution results often are not as good as expected, and many problems in the corporate performance appraisal system have been exposed during the execution. For example, many enterprise managers simply think that performance management is performance appraisal, which results in performance appraisal often being used to “settle the accounts after autumn” in actual work. In addition, the relationship between performance appraisal and employee returns is not strong, which directly affects the enthusiasm of employees at work due to salary differences. Therefore, the application research of the employee performance appraisal management system is the need of the current society. In recent years, employee performance appraisal has attracted the attention of many scholars. The management by objectives method, the key performance indicator method, and the 360-degree performance evaluation method are the most studied evaluation methods in recent years. The comparison table of the three assessment methods is shown in Table 1.

Since the 1990s, the world economy has undergone significant changes, and people have begun to enter the era of the knowledge economy supported by computer technology. Human resources (HR), represented by knowledge workers, has become the object of competition among many high-tech companies. Corporate management focuses on conducting comprehensive performance appraisals and managing human resources. Therefore, for companies in various countries, it has become an urgent task for many companies to establish a comprehensive employee performance appraisal management system suitable for themselves and to provide a basis for corporate human resource strategic decisions.

Many experts and scholars are mainly concerned with the research on corporate performance appraisals. However, the research done in this area is relatively hollow and only talks about the factors that affect the management of human resources assessment from some aspects. It discusses the methods of the assessment process, focusing on the application of efficient assessment methods in the assessment process to make the assessment fairer and more reasonable. However, there is a lack of corresponding technical support, and most of them do not realize the application of the entire system with the help of science and technology but only provide assessment methods. Therefore, in this paper, the application of an improved clustering algorithm based on neural networks in an employee performance appraisal management system provides a reference for future related research on employee appraisal management systems and provides a reference for enterprises. This article is divided into six sections, first introducing the research background and significance of the thesis; then summarizing the research status of the cluster analysis applications; expounding on the theories involved in this research, including human resources, neural networks, clustering methods; and presenting the realization of the employee performance appraisal management system, followed by a summary of the research content and discussion of the advantages of the system.