A Multi-Source Human Resource Data Mining and Enterprise Dynamic Performance Management Framework Based on Hybrid Fuzzy-Bayesian Simulation

Abstract

This study addresses the heterogeneity of multi-source human resource data, the ambiguity of subjective evaluations, the time-varying nature of performance states, and the difficulty of translating predictions into management interventions. A hybrid fuzzy-dynamic Bayesian performance management framework, HFDB-PM, is proposed. The framework aligns employee attributes, the management environment, cognitive-affective states, behavioral responses, and performance outcomes; represents expert evaluations using triangular fuzzy numbers; and integrates theoretical constraints, historical data, and expert knowledge to learn a dynamic Bayesian network. Online updating, backward inference, sensitivity analysis, and multi-period Monte Carlo simulation are further combined to support performance prediction, causal diagnosis, and intervention optimization. Experiments are conducted on a semi-synthetic longitudinal dataset containing 1,284 employees, eight quarters, and 10,272 employee-time-slice records. HFDB-PM achieves an Accuracy of 0.856, a Macro-F1 of 0.838, and an AUC of 0.925, with a Brier Score of 0.098 and an ECE of 0.021. Compared with DBN, Macro-F1 and AUC increase by 3.1 and 2.1 percentage points, respectively, while ECE decreases by 40.0%. In the three-period simulation, the combined strategy of training, transparent feedback, and workload adjustment raises the probability of high performance to 0.468 and reduces the probability of low performance to 0.223, demonstrating the effectiveness of the framework for dynamic prediction, interpretable diagnosis, and management decision support.