Existing studies have constructed knowledge graph （KG） intelligent question-answering systems based on large language models （LLMs） in the field of special equipment. However， limited by the inincomplete entity relationships of KG， LLMs are still prone to hallucination in knowledge-intensive tasks. To suppress the generation of hallucinations， the fusion KG reasoning technology is proposed to enhance the knowledge representation by completing the entity relationship links. Furthermore， in view of the deficiencies of the existing KG reasoning methods in semantic association and topological structure parsing， a dynamic reasoning mechanism based on LLM is further introduced. By leveraging its deep semantic understanding ability， high-order logic rules are automatically generated to achieve the precise expansion of KG， thereby constructing a bidirectional collaborative optimization mechanism between LLM and KG. The results show that this method significantly outperforms the baseline model in terms of mean reciprocal rank （MRR）， first hit rate （Hits@1）， and top ten hit rate （Hits@10） on the Family， Kinship， and UMLS datasets.