Aiming at addressing the issue of highly specialized faulty text and the challenge of sparse character vectors in high-dimensional space resulting from repetitive characters and limited character types, a named entity recognition method based on Domain BERT (DBERT) is proposed. The DBERT model achieves effective dimensionality reduction and refinement of faulty text features by introducing a feature compression strategy. It also undergoes domain-specific pre-training to fully learn and adapt to the unique characteristics and specializations of faulty text. Subsequently, the DBERT model extracts context-related features of characters in the text and combines these features with specific character representations after a weighting operation. Named entity recognition is then performed using a combination of BiLSTM and CRF models. Finally, DBERT-BiLSTM-CRF is compared with LSTM-CRF and BiLSTM-CRF on an automobile maintenance domain dataset, demonstrating superior performance in terms of recall, precision, and F1 score.