Expert Recommendation System Based on Knowledge Graph

This paper proposes an expert recommendation system based on knowledge graph and machine learning technologies, aiming to address the challenge of expert matching caused by equipment failures in the field of intelligent manufacturing. By introducing knowledge graph technology, the system effectively integrates a wide range of engineering knowledge, including expert experience and fault patterns. Combined with the high efficiency of machine learning in processing large-scale data, the system achieves precise matching between experts and fault issues. Compared with traditional manual allocation methods, the proposed system demonstrates significant advantages in automation, efficiency, and intelligence, while improving the accuracy and efficiency of the fault diagnosis process. Experiments show that the system's performance across multiple datasets validates its potential for applications in industrial manufacturing.