We constructed a drug–disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), effectively integrated known drug, disease, side effect and target associations from public database as well as literatures semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extracted literatures semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with attention mechanism.