The traditional human resource management model is difficult to meet the development needs of the digital economy era, and enterprises urgently need to build a digital and intelligent human resource management system. Meanwhile, the integrated integration of assessment and training is of great significance for improving talent management efficiency and optimizing resource allocation. Based on Bayesian network theory, this study constructs a collaborative optimization analysis model for the integration of digitalization of enterprise human resource management and assessment and training. First, an evaluation index system covering five dimensions of human resource planning, recruitment and allocation, training and development, performance management, and labor relations management is established, the causal relationship between the influencing factors is determined by using the explanatory structure model, and the parameters of the Bayesian network are determined by using the maximum a posteriori estimation method. Then, through empirical analysis of 260 human resource management cases, it was found that the probability of safety problems in human resource planning was as high as 82%, and the probability of contract management and employee health and safety was 76% and 77%, respectively. The results show that the system’s integrated co-optimization effect peaks at around 100 hours during the co-optimization process, followed by a second peak at 177 hours. The study verifies the effectiveness of Bayesian network in identifying key influencing factors and evaluating synergistic effects, and provides a scientific decision support tool for enterprises to promote the digital transformation of human resource management.
