In the enterprise’s human resource management, the enterprise’s human resource demand forecast is used by the enterprise as one of the bases of its strategic planning, and the human resource demand forecast indicates the direction for the enterprise’s human resource strategy development, and ensures the scientificity and effectiveness in its implementation. This paper combines the grey correlation model and BP neural network, and optimizes the two models to form a human resource demand forecasting model based on grey BP neural network. The test enterprise is selected, and after the screening of indicators, the model is used to predict its human resource demand. The four indicators that have a great influence on the analysis of human resources demand of the test enterprise are the length of transmission network, the number of substations under the jurisdiction of the company, the total number of users, and the amount of electricity sold on the Internet, and the values of these four indicators in 2023 are 4905.987 kilometers, 193 seats, 690.348 million people, and 55,569.614 million kWh, respectively. Considering the influence of various types of characteristics on the results of the model predictions, the data on the impact of different elements on the demand for human resources were sorted out. It can be seen in the size of the number of recruits of different enterprises in the past few years, the number of recruits is mainly concentrated in the range of 50~200 people, among which the frequency of enterprises recruiting 60-80 people is the highest, with a total of 158 times, and the size of the number of recruits is relatively balanced.
