Abstract: In order to meet the requirement for precise location of fault sources at the meta-component level of shearers and improve mining efficiency and safety controllability of fully mechanized working faces, knowledge graph technology is introduced into the process of dynamic modeling, formal storage and intelligent interaction of shear fault maintenance knowledge. An intelligent maintenance knowledge base of shear based on knowledge graph is constructed. From the aspects of the shearer hardware topology, fault maintenance, sensor monitoring, related terms and naming structures are standardized. The entities, relationships and attributes of the shearer intelligent maintenance knowledge graph are defined and extracted, and a hardware topology sub-network diagram is established,which contains 5 types of entities, including whole machine, component, sub-component, element and part. And a fault maintenance sub-network diagram is established, which contains 5 types of entities, including fault type, location, phenomenon, cause and solution. A sensing monitoring sub-network diagram is established, which contains 2 types of entities, including sensor and monitoring location.Through the process of entity disambiguation and common reference resolution, 3 sub-network diagrams, including hardware topology, fault maintenance and sensor monitoring, are merged to form a network diagram of intelligent maintenance knowledge of coal shearers. And the interaction relationship between network nodes is formally described to express intelligent maintenance knowledge. Using Neo4j, Py2neo and other technologies, a prototype system of intelligent maintenance knowledge base of shearer that can realize dynamic interaction is built, and the functions of fault information retrieval and technical guidance are realized initially.