Aiming at the problem of insufficient extraction of defective text feature information and insufficient accuracy of defective text classification in the task of knowledge mining and analysis of defective text information of electric power equipment

a kind of two-branch feature fusion based on BERT (bidirectional encoder representations from transformers) is proposed as a text classification model for electric power equipment defective texts. Firstly

the defective text data is preprocessed to delete abnormal defective text and summarize the characteristics of defective text of power equipment. Then

the BERT model is adopted as a text encoder

and the text is transformed into vectors and input into BiLSTM-Attention (attention-based bidirectional long short- term memory) module and multi

-branch CNN (multi-scale convolutional neural network

MCNN) module to extract the semantic information features and key information features of the defective text; Finally

the extracted semantic features and multi-dimensional key feature vectors are fused and the Softmax layer is used to achieve the defective text classification of the defective text. Compared with the benchmark model BERT-BiLSTM-Attention

its accuracy

recall

and

F

1

value are improved by 2.76%

3.58%

and 4.39%

showing the superior performance of the proposed model in defective text categorization task.