Medical X-rays,as a routine examination method for chest diseases,can diagnose early and unobvious chest diseases and observe the lesions.However,the charact eristics of multiple diseases on the same radiographic image are a challenge to the classifi cation problem. In addition,there are different correspondences between disease labels,which f urther leads to the difficulty of classification tasks.In response to the above problems,this paper combines the graph convolutional neural network (GCN) with the traditional convolutional neural network (CNN),and proposes a multi-label chest radiographic image disease classification method that co mbines label features with image features.This method uses the graph convolutional neural ne twork to model the global correlation of the labels,that is,constructs a directed relat ionship graph on the disease label,each node in the directed graph represents a label category, and then inputs the graph into the graph convolutional neural network to extract the label featu res,and finally merges with the image features to sort.The experimental results of the method p roposed in this paper on the ChestX-ray14 dataset show that the average AUC of 14 chest disease s reaches 0.843.Compared with the current three classic methods and advanced methods,the method in this paper can effectively improve the classification performance.