Abstract: The primary objective of this study is to apply knowledge extraction techniques in the field of microwave technology to optimize the debugging and assembly processes of microwave components. For inadequacies of knowledge extraction techniques in the domain of microwave technology, a knowledge extraction algorithm based on the CasRel-LN model is proposed. This model comprehensively incorporates the BERT encoder, subject tagging model and object tagging model under specific relationship contexts and successfully achieves entity extraction of microwave component fault texts. Experimental results demonstrate that the CasRel-LN model, which enhances the precision and recall of entity extraction, exhibits superior overall performance on the test set compared to other knowledge extraction models. A knowledge graph of microwave component faults is constructed by this algorithm. This knowledge graph comprises 1 568 entities and 1 618 triplets, which are stored in the Neo4j graph database. Through the visual representation of the knowledge graph, debugging personnel can efficiently solve complex issues in the debugging of microwave components and thus ensure the stable operation of electronic devices. Fault extraction and debugging can provide more effective support for achieving better performance during the assembly phase.