Chemical accident news data contains information such as news content, titles, and news sources. The text of news content is highly dependent on the context. In order to extract text features more accurately and improve the accuracy of chemical accident classification, this study proposes a Bidirectional LSTM (BLSTM-Attention) neural network model based on Attention mechanism to extract features of chemical news texts and realize text classification. The BLSTM-Attention neural network model can combine text context semantic information to extract text features of accident news through forward and reverse angles. Considering that different words have different contributions to the text in the accident news, the Attention mechanism is added to assign different weights to different words and sentences. Finally, the proposed classification method is compared with Naive-Bayes, CNN, RNN, BLSTM classification method on the same chemical accident news data set. Experimental results show that the BLSTM-Attention neural network model proposed in this study is better than other classification models in chemical data set.