Traditional chest-aided diagnosis systems have poor image feature extraction effects and low average accuracy in disease classification based on chest X-ray images. In view of these problems, a multi-level classification network that combines an attention mechanism and label correlation is proposed. The training of the network is divided into two stages. In stage one, in order to improve the feature extraction capability of the network, an attention mechanism is introduced, and a two-branch feature extraction network is constructed to realize the extraction of comprehensive features. In stage two, according to the correlation between labels and other issues in multi-label classification, a graph convolutional neural network is used to model the label correlation, which is then combined with the feature extraction results obtained in stage one, so as to achieve the multi-label classification task of diseases based on chest X-ray images. The experimental results show that the weighted average AUC of diseases by the proposed method on the ChestX-ray14 dataset reaches 0.827. Therefore, the method can assist doctors in diagnosing chest diseases and has certain clinical application value.