The multi-label text classification is one of the important branches of multi-label classification. Existing methods often ignore the relationship between labels, and thus the correlation between labels can hardly be put into effective use, which affects the effects of classification. On this basis, this study proposes a hybrid BERT and graph attention (HBGA) model that fuses BERT and the graph attention network. First, BERT is employed to obtain the context vector representation of the input text, and Bi-LSTM and the capsule network are used to extract the global and local features of the text, respectively. Then, through feature fusion, text feature vectors are constructed. Meanwhile, the correlation between labels is modeled through graphs, and the nodes in graphs are used to represent the word embedding of the labels, and these label vectors are mapped to a set of interdependent classifiers through the graph attention network. Finally, the classifiers are applied to the text features obtained by the feature extraction module for end-to-end training. The classifier and feature information are integrated to obtain the final prediction results. Comparative experiments are performed on datasets Reuters-21578 and AAPD, and the experimental results indicate that the model in this study has been effectively improved on tasks of multi-label text classification.