Design of Employee Turnover Rate Prediction and Intervention Algorithm Based on Artificial Neural Network Model

Talent is a key force for the vigorous development of an enterprise, and talent loss can lead to a decrease in the competitiveness of the enterprise, thereby affecting its development. Most modern enterprises place too much emphasis on the interests of the company and neglect the interests of employees, resulting in employees not receiving corresponding compensation, high work pressure, and a high turnover rate. Therefore, predicting the probability of employee turnover and guiding those who have a tendency to resign to avoid talent turnover has become extremely important. Grasping the trend of employee turnover and understanding the reasons for employee turnover plays a positive role in formulating talent retention measures and improving the rational allocation and management of human resources in enterprises. Based on this, this article designs a prediction and intervention algorithm for employee turnover rate based on artificial neural network (ANN) model, tests the turnover intention of enterprise employees, analyzes the reasons for high turnover rate of employees, and formulates corresponding solutions to enable enterprises to achieve stable and healthy development. The experimental results indicate that the method designed in this article can help human resource managers in enterprises analyze the reasons for employee turnover, identify employees with a tendency to resign in advance, and have certain reference value for solving the problem of employee turnover in enterprises.