Aiming at the problems that the existing consultation recommendation methods do not fully utilize the rich dialogue information between doctors and patients and do not capture patients’ real-time health needs and preferences， a consultation recommendation method based on Knowledge Graph and Dialogue Structure （KGDS） was proposed. Firstly， a medical Knowledge Graph （KG） including comment sentiment analysis and professional medical knowledge was constructed to improve the fine-grained feature representations of doctors and patients. Secondly， in the patient representation learning part， a patient query encoder was designed to extract key features of query text at both word and sentence levels， and to improve the higher-level feature interactions between doctor and patient vectors through attention mechanism. Thirdly， a diagnosis dialogue was modeled to make full use of the rich dialogue information between doctors and patients to enhance the doctor-patient feature representation. Finally， a dialogue simulator based on contrastive learning was designed to capture the dynamic needs and real-time preferences of patients， and the simulated dialogue representation was used to support recommendation score prediction. Experimental results on a real dataset show that compared with the optimal baseline method， KGDS increases AUC（Area Under the Curve）， MRR@15（Mean Reciprocal Rank）， Diversity@15， F1@15， HR@15 （Hit Ratio） and NDCG@15（Normalized Discounted Cumulative Gain） by 1.82， 1.78， 3.85， 3.06， 10.02 and 4.51 percentage points， respectively， which verifies the effectiveness of the proposed consultation recommendation method， and it can be seen that adding sentiment analysis and KG improves the interpretability of the recommendation results.