This study aims to address the problem of named entity recognition (NER) in wind turbine fault texts. Currently, most wind turbine fault texts are characterized by an abundance of technical terms, significant long-distance dependencies, and unclear entity boundaries. Therefore, we propose an improved WC-LSTM model (GR-WCM). First, we use a self-attention mechanism to encode lexical information, capturing all lexical information for word-character fusion. Second, we introduce a Global Context Mechanism (GCM) that integrates the representation of the entire future and past sentences into each unit of the Bi-LSTM framework to obtain semantic features of long sequences. We also employ the R-drop training algorithm during model training to further enhance the model’s generalization ability. Finally, we use Conditional Random Fields (CRF) for sequence decoding to obtain the globally optimal sequence. Experimental results on a self-built wind turbine fault dataset and the Resume dataset show that, compared with the baseline WC-LSTM model, the proposed method improves the F1 score by 2.12% and 0.77%, respectively, effectively enhancing the accuracy of Chinese NER.