With the development of natural language processing technology，the intelligent retrieval and question-answering system continuously developed.In order to improve the semantic parsing ability of traditional knowledge graph and the problem that the current general large language model is not deep enough for knowledge learning in the vertical domain，a large language model method integrating knowledge graph is proposed，and a two-step optimization is carried out.First，on the basis of named entity recognition and relationship extraction，Build a large model Prompt template for auxiliary enhancement generation，using the data stored in the map to provide relevant sources.Second，the low-rank adaptation （LoRA） strategy is used to freeze the original parameters of the large model，and some network parameters are added for fine-tuning training，so as to optimize the knowledge reserve and understanding of the model in the field of aerospace tracking，telemetering，and command （TT&C）.Through the two-step improvement，the overall semantic analysis and knowledge details of the model are improved.Combined with the relevant textbooks，reports，and manuals in the field of aerospace TT&C，a knowledge question-answering system is built，and good results are obtained，indicating that the method has certain application value.