摘要：As the company’s workforce continues to expand, finding key features related to employee performance, quickly identifying high-potential employees, and predicting a rise in turnover are hot spots for research. This paper first analyzes the key characteristics of dataset performance and applies deep learning to identify high-potential employees and predicts the rise of separation. Compared with traditional machine learning methods, it can be seen that deep learning applications have a greater improvement. The aim is to provide a new idea for the intersection of human resources and computer AI. In the preparation of this article, a large number of companies’ desensitized employee data were collected in the real industry, including job, performance, education, and data communication between employees. Firstly, an interactive network-based employee topology map was established. According to the large amount of data collected from the real industry, the key characteristics of employee performance were analyzed, and a series of models were compared to traditional machine learning methods and deep learning calculation indicators, including accuracy, AUC and other indicators.
