To overcome the difficulty of knowledge extraction from unstructured data in the field of military equipment， this paper proposes a knowledge extraction method based on retrieval-augmented generation （RAG） using hybrid search. First， a large language model is used to assist in constructing an ontology model. On this basis， knowledge is extracted from semi-structured data with reference to the constructed ontology model to form triple data， and the extracted results are used to construct a database. Then， for unstructured data， a novel hybrid search method is proposed. This method integrates sparse retrieval and dense retrieval methods to retrieve similar knowledge blocks as reference examples for prompt design. Finally， prompts for knowledge extraction in the field of military equipment are designed， based on which a large language model is used to extract knowledge from unstructured data. The results show that the proposed method is capable of extracting knowledge from unstructured data. Compared with knowledge extraction without the RAG framework and knowledge extraction based on the RAG framework without hybrid search， the proposed method extracts a larger number of triples and achieves a higher recall rate.