People's awareness about their nutrition habits is increasing.Keeping track of what we eat will be helpful for us to follow a healthier diet.Currently,nutrient recognition of food images is mainly focused on food categories recognition,or is tackled as multi-label task recognition.These two approaches,however,are not very discriminative owing to their neglect of potential relationship between ingredients.In this paper,we introduce the relationship between ingredients to identify food nutrients based on previous work.The recognition approach includes two modules,namely the image feature extraction module and the ingredients relationship learning module.The low-dimensional image feature vectors are extracted by convolutional neural network (CNN),and the relationship between ingredients is learned through a graph convolutional network (GCN).Specifically,GCN uses graph data where nodes represent food ingredients as word embedding and edges represent the correlation between nodes.Then the GCN directly map the graph data into a set of interdependent classifiers.Finally,the low-dimensional image feature vectors are fused to make detailed classification.We conducted experiments on food data sets of Food-101 and VireoFood-172.Compared with state of the art food recognition methods,our GCN-based multi-label food image classification method offers very promising results and can effectively improve the recognition performance.