Aiming at the characteristics of domain knowledge graphs with strict schema layers and rich attribute information, a method of domain knowledge graph completion incorporating concept and attribute information is proposed. Firstly, the concepts in the schema layer of the domain knowledge graph are represented by embedding using the HAKE model which can model semantic hierarchical structures to build a concept-based instance vector representation. Then, a distinction is made between instance triples and attribute triples for the data layer, and an attribute-based instance vector representation is obtained by incorporating the attributes and concepts of the instance through the attention mechanism. Finally, the concept-based and attribute-based instance vector representations are jointly trained to achieve scoring of the instance triples. Experiments are conducted using the knowledge graph constructed based on the DWY100K dataset, the medical knowledge graph MED-BBK-9K and the knowledge graph constructed based on equipment fault diagnosis data of a steel enterprise, and the experimental results show that the performance of the proposed method in domain knowledge graph completion is better than the existing knowledge graph completion methods.