Construction of Enterprise Human Resource Planning Model Based on BP Neural Network Algorithm

The degree of matching between personnel and positions directly affects the reasonable utilization of other resources and the overall allocation efficiency of the enterprise. It is a key factor determining whether the enterprise can sustain, stabilize, and develop rapidly. At this time, many enterprises are increasingly paying attention to specific human resource management, and their investment costs and enthusiasm are also constantly strengthening. For a company, its main development focus is serious planning, and efforts are being made to promote the better completion of resource planning by enterprise talents. This article utilizes the nonlinear and adaptive characteristics of the BPNN (BP Neural Network) algorithm to establish a BP network model, discover its potential laws, and construct a human resource planning model for enterprises. Finally, research has shown that in the application of the BPNN algorithm in a certain company, although the output values of the BP network are relatively low, they generally fluctuate slightly between 0.857 and 0.952, with high stability. This article believes that it meets the decision-making requirements. It can be seen that BPNN has good ability to approximate nonlinear functions and exhibits great charm in predicting future discontinuous changes, with broad application prospects.