Airborne products with diverse range and extensive industrial chain have a complex testing system， requiring comprehensive metrological verification work. However， airborne product data resources primarily exist in unstructured， fragmented， and multimodal forms， making it difficult to conduct overall analysis of various testing elements or trace the standardization of testing and product quality under a unified framework， thereby posing challenges to metrological work. To address this issue， the construction of knowledge graph for Metrological Traceability of Airborne Products （MT-AP） was explored by combining generative Large Language Model （LLM）. Firstly， the resource types and metrological traceability links were sorted out， and a Knowledge Graph （KG） ontological model was constructed. Secondly， LLM-based work modules were designed and integrated into workflow chains. Finally， a method for constructing the MT-AP knowledge graph based on the workflow chains and prompt templates was proposed. Experiments were conducted using airborne product instance data and workflow chains. Experimental results show that the proposed method has the knowledge comprehension and naming capability scored above 0.91 basically， the text segmentation and knowledge decoupling capability scored above 0.83 basically， and the complex parameter extraction and structured capability scored above 0.85 basically. It can be seen that the proposed method exhibits satisfactory performance in key tasks of MT-AP knowledge graph construction， providing technical support for metrology engineering of airborne products.