As healthcare popularity increases, more people use food therapy for nourishment and healing. However, without scientific guidance, it's difficult to select appropriate foods for specific needs. To address the issue, we extract knowledge from TCMSP and professional books and fuse the data from different sources. Next, the Food Therapy Knowledge Graph (FTKG) is constructed. Finally, a food therapy system is developed that integrates the concept of TCMSP and FTKG, which uses the efficient knowledge retrieval and knowledge reasoning ability of the knowledge graph. It provides scientific food therapy solutions by analyzing symptoms and substituting traditional Chinese medicine with food, s address individual health needs.

Traditional Chinese medicine (TCM) has a unique advantage of preventive treatment of diseases, and adopting the concept of early intervention can effectively prevent diseases. Using knowledge graph is an effective way while the knowledge in the field of TCM is huge and messy. However, the structure of the TCM knowledge graph is often relatively sparse, which makes it highly limited. To this end, a rule-based compositional representation learning (RCRL) model is proposed. RCRL uses the implicit rules in the TCM knowledge graph, which solves the problem of poor representation learning due to the sparse structure of the TCM knowledge graph to a certain extent. Extensive experiments are conducted on the TCM knowledge graph and public datasets, and they are compared with other baselines. Experimental results show that RCRL is superior to other baselines, with improved learning accuracy and interpretability, and can be used for various downstream tasks.