Analysing human-computer interaction behaviour in human resource management system based on artificial intelligence technology

Abstract

The aim is to optimise the procedures and reduce the workload of human resource management (HRM), thereby increasing the working efficiency and improving system performance. Deep learning (DL) algorithms are employed to build a CC neural network (BPNN)-based HRM system model. Then, this model is optimised and simulated, whose performance is verified through comparisons with other models. The comparative simulation demonstrates that the proposed system model converges the fastest. Results of the Leave-One-Out (LOO) method also prove the fastest convergence and the best optimisation effect of the proposed system model over classic models. In particular, it can converge at about 60 epochs and provides an accuracy of about 88.72%, 2.76% higher than other models at tops. Regarding the prediction performance, the proposed system model presents an excellent fitting effect. Through experiments, the constructed model converges faster and makes predictions more accurately, providing an experimental reference for the operation and intelligent development of HRM systems in the economic field in the future.