With the development of technologies such as big data and cloud computing, the scale of data in the food domain is growing at an astonishing rate. These data not only come from diverse sources and have complex structures, but also lack standardized terminology, which poses challenges to the effective integration and utilization of food-related data. Knowledge graphs, as fundamental cornerstone of achieving general artificial intelligence, provides support for the organization and management of food data and its higher-level applications in terms of integration and semantic understanding. By summarizing recent research achievements of knowledge graphs in the food domain, the construction methods of knowledge graphs in food domain was reviewed, covering key steps such as ontology construction, knowledge extraction, knowledge fusion, and processing. The current applications of knowledge graphs in the food domain, particularly in three areas, food nutrition and health, food innovation and research, and food safety and traceability. Based on current state of development of knowledge graphs in the food domain, incorporating multimodal data fusion technology, large language model construction, and the intelligentization of industrial equipment in the food field, the future development directions of knowledge graphs in food domain were anticipated.