To fully explore the intrinsic correlation value of massive data， comprehensively and accurately construct the knowledge graph in the field of early warning equipment， a knowledge graph construction method of early warning equipment based on deep learning is proposed. The proposed method takes typical unstructured text data as the research object， constructs the knowledge graph ontology and professional dictionary in the field of early warning equipment， and obtains the early warning equipment knowledge dataset containing 6 468 entity samples and 11 216 relationship samples by driving word segmentation. The entity recognition and relationship extraction are performed based on the knowledge extraction method of multiple deep learning models. The experimental results show that the proposed method has excellent performance in the field of early warning equipment， with the F₁ value of the entity recognition model reaching 91.54% and the F₁ value of the relationship extraction model reaching 91.05%. The extracted entity relationship triples are stored in the Neo4j graph database， and the early warning equipment field knowledge graph composed of 14 entities and 22 relationships is further constructed and visualized.