| Summary: | In the field of military research, manufacturing and management of weapons and equipment are very important. Due to the continuous advancement of science and technology, many military equipment databases have a loose structure, which makes them difficult to be utilized efficiently, resulting in low efficiency, chaotic management, and other issues. In order to solve these problems, an entity-relation extraction method based on CRF and syntactic analysis tree is proposed according to the latest text extraction algorithm. Finally, a military knowledge graph construction method is optimized via massive data training, model comparison and improvement. The ternary data extraction method is significantly better than the single algorithm extraction method, and the accuracy of the extracted training model can reach 72%. Compared with the traditional entity-relation extraction method, the accuracy of the entity-relation extraction method based on the fusion of CRF and syntax analysis tree is improved by 12.6% when the confidence model is added, and the comprehensive evaluation accuracy can reach 78.11%. This result has significant practical value for the construction of knowledge graphs in the field of military equipment.
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