A Named Entity Recognition Method for Chinese Vehicle Fault Repair Cases Based on a Combined Model

This paper addresses the inefficiency of manually screening fault knowledge in Chinese vehicle repair cases and proposes a NER method based on a combined model aimed at efficiently extracting automotive fault knowledge entities from unstructured vehicle repair case texts. First, the data characteristics of historical vehicle repair cases are analyzed, and in response to issues such as the misuse and overuse of punctuation and redundant text, long-text segmentation rules are designed, and text classification is performed using the Text-CNN method. Second, to address the low recognition accuracy of traditional methods for non-continuous and nested entities, a BERT-BiLSTM-CRF model is used to independently recognize entity categories and relationships. An entity relationship matching database is constructed, and methods and algorithms for non-continuous entity combination are designed. Finally, named entity fusion based on text similarity is employed to recognize automotive fault knowledge entities in vehicle repair case data. The results demonstrate that this method can effectively identify named entities related to automotive fault knowledge in Chinese vehicle fault cases.