National Food Safety Standards(NFSS) are not only the operation specifications of food producers, but also the law enforcement criteria of food safety supervision. However, there are various NFSSs with a wide range of contents and complicated inter-reference relationships. To systematically study the contents and structures of NFSSs, it is necessary to extract the knowledges and mine the reference relationships in NFSSs. First, the contents of the standard files and the reference relationship between the standard files were extracted as knowledge triplets through the Knowledge Graph(KG) technology, and the triplets were used to construct the NFSS knowledge graph. Then, this knowledge graph was linked to the food production process ontology which was made manually based on Hazard Analysis Critical Control Point(HACCP) standards, so that the food safety standards and the related food production processes can be referenced to each other. At the same time, the Louvain community discovery algorithm was used to analyze the standard reference network in the knowledge graph, and the standards with high citations as well as their types in NFSSs were obtained. Finally, a question answering system was built using gStore's Application Programming Interface(API) and Django, which realized the knowledge retrieval and reasoning based on natural language, and the high-impact NFSSs in the graph could be found under specified requirements.