A data-driven model for human resource strategy development in higher education in a machine learning framework

Abstract

Various fields in real life have increasingly utilized machine learning methods and data mining technology in recent years. This paper creates a data-driven model to implement intelligent human resource management in colleges and universities. The model utilizes the fuzzy decision tree algorithm to assist colleges and universities in selecting the most suitable talents and completing the talent recruitment and selection process within a short timeframe. Additionally, it utilizes the ID3 algorithm to filter out potential staff departures and factors, enabling targeted decision-making to prevent talent loss. After the implementation of the human resource management strategy based on the model, the completion rate of the recruitment plan increased by 15.1% compared with the pre-implementation rate, the turnover rate decreased by 3%, and 91% of the employees agreed with the management of the Human Resources Department. This paper’s model provides a reliable personnel decision-making and management strategy for human resource management in universities.