For chemical safety issues， Named Entity Recognition （NER） for chemical safety accidents can identify information effectively such as location of the accident， type of chemicals involved in the accident， and people responsible for the accident. In order to solve the problems of diversified information and inability to fully utilize word information in NER in the field of chemical safety accidents， a fusion network model of SoftLexicon used lattice Long Short-Term Memory （LSTM） network combined with self-attention mechanism was proposed. Firstly， the input sentence was extended to character level， and in the process of introducing word information， external lexicon resources were combined to construct character features. Then， the character features were introduced into the Lattice-LSTM-CRF （Conditional Random Field） layer of self-attention mechanism and combined with the pre-trained model BERT （Bidirectional Encoder Representations from Transformers） for entity recognition. Experimental results prove that the F1 score of the proposed model in chemical industry dataset reaches 88.61%， and the model' indicator values on public datasets such as Weibo and resume are better than those of the mainstream models such as word-level BiLSTM-CRF and Lattice-LSTM. It can be seen that the proposed model can complete NER tasks in the field of chemical safety accidents effectively.