Aiming at the problems of incomplete information dimension， sparse interaction data and redundant interaction information in recipe recommendation tasks， a Recipe recommendation model based on hierarchical learning of Flavor embedding heterogeneous graph （RecipeFlavor） was proposed. Firstly， the flavor molecule dimension was introduced， and a heterogeneous graph was constructed on the basis of users， foods， ingredients and flavor substances of ingredients to represent the connection among four kinds of nodes effectively. Then， a hierarchical learning module based on heterogeneous graph was constructed on the basis of information transmission mechanism， and combined with Squeeze Attention （SA） mechanism， different node relationships were regarded as different information channels， so that key interaction information between nodes was extracted and noise was suppressed. Finally， a Contrastive Learning （CL） module was constructed on the basis of feature-aware noise， and positive and negative sample discrimination tasks were introduced in model learning， thereby enhancing the information associations among users and recipe nodes and improving the model’s learning ability for features. Experimental results show that compared with HGAT （Hierarchical Graph ATtention network for recipe recommendation） model on Recipe 1M+ large dataset， RecipeFlavor has the Area Under the ROC Curve （AUC） increased by 1.44 percentage points， and the model Precision （Pre）， Hit Rate （HR）， Mean Average Precision （MAP）， and Normalized Discounted Cumulative Gain （NDCG） of Top-10 increased by 0.76， 6.11， 2.68， and 3.05 percentage points， respectively. It can be seen that the introduction of flavor molecule information expands the learning dimension of recipe recommendation， and RecipeFlavor can extract key information in heterogeneous graph effectively， and enhance correlation among users and recipes， and thus improving the precision of recipe recommendations.