Enterprise human resource performance evaluation method based on knowledge graph
By improving the Mamba architecture and neural logic reasoning network, and combining multi-source heterogeneous data with federated knowledge distillation methods, the problems of data silos and insufficient feedback in enterprise human resource performance evaluation are solved, realizing multi-dimensional and semantic enterprise-level performance evaluation and improving the accuracy and stability of the evaluation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIANGSU VOCATION & TECHNICAL COLLEGE OF FINANCE & ECONOMICS
- Filing Date
- 2025-06-10
- Publication Date
- 2026-07-21
AI Technical Summary
Existing enterprise human resource performance evaluation methods suffer from problems such as single data dimensions, lack of semantic feature analysis, cross-departmental data silos, and insufficient feedback mechanisms, resulting in highly subjective evaluation results, low accuracy, and inability to achieve dynamic optimization.
By adopting an improved Mamba architecture and neural logic reasoning network, and through multi-source heterogeneous data acquisition, deep semantic feature extraction, dynamic logic rule learning, and federated knowledge distillation methods, cross-departmental collaborative analysis and feedback optimization are achieved. A multi-granularity logic reasoning knowledge graph library is constructed for enterprise-level global dynamic performance evaluation.
It enables multi-dimensional and semantic modeling of employee performance evaluation, improves the objectivity and accuracy of evaluation results, solves the problem of cross-departmental data silos, enhances the comprehensiveness and dynamism of evaluation, reduces evaluation errors, and improves the accuracy and stability of evaluation results.
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Figure CN120672196B_ABST