A large number of typical fault cases are accumulated in the information work of the national grid company

but it is difficult to use automation means to understand and analyze the text data.We utilize the text mining technology to extract the problems and causes of defects from typical fault texts to convert to the causal relationship of malfunction

which provides the necessary conditions for fault feature extraction.The concrete method is to first turn the problem of extracting causation into the problem of three categories of sentences.This method can narrow the target set

and improve the accuracy rate.Then the sentence is expressed in distributed text

and the bidirectional long short term term（Bi LSTM） classification model is used to extract the deep semantic features of the event sentence.The experimental results show that Bi LSTM is more effective in the classification of fault text sentences than LSTM

and the convolution neural network（CNN）

the feature of fault and reason information in text is more accurate

and the f1-score can be up to 67%.