Employee Performance Prediction Model Based on BP Neural Networks

The progress of artificial intelligence, employees performance prediction based on data has brought significant benefit of making work about human resource management and decision making more effectively. The conventional approaches to employees performance evaluating, such as KPI and 360 evaluation system, sometimes falls short of describing complex employees data and capturing the multi-faceted nature of employees performance. In this study, we propose a novel employee performance prediction model based on Backpropagation (BP) neural networks aiming to accurately predict employees performance by learning the complex features in an automatic way. The performance of this model is tested via the employees data from 514 employees of 82 small- and middle-sized enterprises (SMEs) of various industries of retail, hospitality, consulting, manufacturing and finance. The performance prediction results of this model show that compared to the traditional evaluating methods, the BP neural network model prediction can significantly improve the performance predicting ability of employee evaluation, especially in the various performance aspects, which shows that BP neural networks model could play more efficient and efficient part than other models of employees performance predicting. The feasibility of BP neural networks model of employees performance predicting can be demonstrated by this study. Also, this study provided better supporting decision-making approach for human resources management for all industries.