Multi-label text classification methods based on deep learning lack multi-granularity learning of text information and the utilization of constraint relations between labels. To solve these problems, this study proposes a multi-label text classification method with enhancing multi-granularity information relations. First, this method embeds text and labels in the same space by joint embedding and employs the BERT pre-trained model to obtain the implicit vector feature representation of text and labels. Then, three multi-granularity information relations enhancing modules including document-level information shallow label attention (DISLA) classification module, word-level information deep label attention (WIDLA) classification module, and label constraint relation matching auxiliary module are constructed. The first two modules carry out multi-granularity learning from shared feature representation: the shallow interactive learning between document-level text information and label information, and the deep interactive learning between word-level text information and label information. The auxiliary module improves the classification performance by learning the relation between labels. Finally, the comparison with current mainstream multi-label text classification algorithms on three representative datasets shows that the proposed method achieves the best performance on main indicators of Micro-F1, Macro-F1, nDCG@k, and P@k.