Multi-Label Text Classification （MLTC） is one of the important subtasks in the field of Natural Language Processing （NLP）. In order to solve the problem of complex correlation between multiple labels， an MLTC method TLA-BERT was proposed by incorporating Bidirectional Encoder Representations from Transformers （BERT） and label semantic attention. Firstly， the contextual vector representation of the input text was learned by fine-tuning the self-coding pre-training model. Secondly， the labels were encoded individually by using Long Short-Term Memory （LSTM） neural network. Finally， the contribution of text to each label was explicitly highlighted with the use of an attention mechanism in order to predict the multi-label sequences. Experimental results show that compared with Sequence Generation Model （SGM） algorithm， the proposed method improves the F value by 2.8 percentage points and 1.5 percentage points on the Arxiv Academic Paper Dataset （AAPD） and Reuters Corpus Volume I （RCV1）-v2 public dataset respectively.