Abstract: Addressing the pervasive issue of disparate, non-systematic, and inadequately correlated defect text generated during the operation and maintenance of electrical equipment, a novel model for the recognition of electrical equipment defect text is put forth. The bidirectional encoder representation from transformers（BERT） model predicated on whole word masking is utilized, supplanting the traditional BERT model premised on random masking, which augments the comprehension of electrical lexicon. The integration of bidirectional long short-term memory（BiLSTM） into the model fortifies the capacity to apprehend contextual information, bolstering the model’s robustness. Moreover, the incorporation of the Attention mechanism enables the model to capture sophisticated dependencies between entities of electrical equipment defects, thereby further enhancing the model’s performance. Empirical results corroborate that the accuracy, recall, and F1 score of the model are an impressive 96. 26%, 96. 94% and 96. 60% respectively. Furthermore, the F1 scores for the location, defect content, and equipment entities all surpass those of competing models, underscoring the superiority and efficacy of the proposed model.