Research on the Construction of Human Resource Demand Forecasting Model Based on Big Data Analysis

This study focuses on the application of big data analysis in human resource demand forecasting, aiming to build an effective forecasting model to cope with the demand changes in human resource flow areas. This paper proposes a new model for predicting human resource demand by processing and analyzing human resource related big data based on the K nearest neighbor nonparametric estimation method. In the study, a system simulation verification was carried out by designing an algorithm adapted to the big data environment to test the effectiveness and reliability of the model. During the simulation process, the focus was on the changing laws of human resource flow areas, and the prediction accuracy and adaptability of the model were evaluated through precise data analysis. The research results show that the constructed model can better capture the dynamic changes of human resource demand, especially in the complex background of regional human resource flow, and the prediction effect is excellent. This study not only enriches the application of big data analysis in forecasting models at the theoretical level, but also demonstrates the operability and promotion value of the model in practice.