Neural-symbolic federated enhanced human resource intelligent decision-making system: integration of multimodal causal reasoning and quantum privacy computation

Abstract Large central enterprises face significant challenges in the digital transformation of human resource management, including severe data silos in human resources, insufficient privacy protection, and the inability of static models to effectively capture dynamic organizational changes. To address these issues, this study proposes the ‘Federated Hypergraph-Dynamic Knowledge Graph’ fusion framework (Fed Hyper KG-HR), which integrates hypergraph neural networks with federated learning to overcome data isolation, enhance privacy compliance, and support dynamic decision-making. The framework innovatively embeds hypergraph theory into federated learning and designs a privacy-enhanced distributed training architecture, achieving 85% cross-organizational collaborative recognition accuracy with zero data leakage under a privacy budget of ε = 1.2. This research also constructs a four-dimensional spatiotemporal ontology model and combines it with a temporal graph database to track employee skill development. Simultaneously, it develops an interpretable decision engine with a response time of 50 milliseconds for turnover early warning. Empirical validation on Group G shows the framework attains a talent turnover prediction F1-score of 0.89 (24% higher than traditional models) and a 93.8% promotion prediction accuracy, with the turnover false alarm rate reduced to 2.8%. The research also designs an adaptive privacy-utility trade-off mechanism, improving decision efficiency by 35% compared with homomorphic encryption. This research boasts both theoretical and practical significance: it fills the methodological gap in complex organizational relationship modeling and offers a systemic digital solution for enterprise HRM. Future research will explore quantum federated learning acceleration to advance HR digitalization toward higher-level intelligence.