The Application of Deep Learning in Human Resource Management: A New Perspective on Employee Recruitment and Performance Evaluation

Abstract

This article explores the application of deep learning in human resource management, focusing on new perspectives and impacts in the fields of employee recruitment and performance evaluation. Through literature review, theoretical framework construction, empirical research, and outcome discussion, it has been found that deep learning has significant advantages in employee recruitment and performance evaluation. In terms of employee recruitment, in-depth learning has improved the accuracy and efficiency of candidate selection, and provided comprehensive evaluation and selection decision support through Natural language processing and facial expression recognition technologies. In terms of performance evaluation, deep learning is based on multi-source data for performance analysis and employee potential prediction, providing personalized performance feedback and incentive measures. The application of deep learning in human resource management has a positive impact on organizational performance and employee development, improving the accuracy and efficiency of recruitment and evaluation, optimizing decision-making processes, and enhancing organizational competitiveness and employee development opportunities. However, deep learning still faces some challenges and limitations in human resource management, such as data privacy and security issues, as well as the demand for technical and professional talents. The conclusions of this study provide guidance and suggestions for the application of deep learning in employee recruitment and performance evaluation for human resource managers, helping them cope with management challenges and achieve long-term organizational development goals.