## 1 Introduction
[13] introduced CNN on the basis of BiLSTM-CRF model framework to capture global context feature information and local key information in parallel and then fused feature information through multi-head attention mechanism to successfully identify relevant entities in the field of subway vehicle equipment, which alleviated the problem of insufficient features in that field. However, the above models use the static word vector generated by the Word2 Vec model as input, which cannot represent Chinese polysemous words and affects the recognition effect of the model [14; 15]. The pre-training model BERT [16] proposed by [PERSON] et al. of Google and the subsequent improved pre-training language model can capture character position information and bidirectional feature relationship in the corpus, dynamically generate context-related word vectors, effectively solve the problem of Chinese polysemy, and are widely used in NER tasks. Studies [17; 18] have introduced the bidirectional pre-training language model BERT to dynamically obtain word vectors, then capture the context bidirectional feature information and decode and identify related domain entities through BiLSTM-CRF, which solves the problem of low accuracy of biomedical and earthquake emergency text entity recognition. [PERSON] et al. [19] used the BERT pre-training model to obtain word vectors containing position information, extract global and local features with BiLSTM and IDCNN, respectively, and connect them, and identify COVID-19 epidemiological entities through conditional random field decoding, which solves the problem of insufficient local feature extraction. [PERSON] et al. [20] proposed the model framework of MacBERT-BiLSTM-CRF for audit texts and introduced adversarial training, which takes the named entity recognition task as the main task and the Chinese word segmentation task as the auxiliary task and shares the word vector boundary information to help the model identify the entity boundary, which solves the problem of unclear entity boundary recognition in audit texts. In view of the problem that Chinese pre-training models lack two factors of Chinese glyph and pinyin, [PERSON] et al. [21] integrated the visual features of glyph and pronunciation features into the pre-training stage of this model to improve its understanding ability of Chinese grammar and semantics. Deep learning methods using pre-trained models and incorporating external feature information have continuously achieved better results than traditional models in named entity recognition tasks, gradually becoming a hot topic of current research. Based on external dictionary information and the BERT model, the works of [22; 23], respectively, used bidirectional maximum matching strategy and an attention mechanism to integrate external dictionary features into dynamic word vectors, improve the recognition accuracy of the model for rare or unknown entities, and enhance the performance of the model for entity recognition in the agricultural field. [PERSON] et al. [24] created a word-splitting dictionary in the field of automobile production equipment faults, extracted information by using part-of-speech features, and fused the dynamic word vectors generated by BERT to obtain joint word vectors, which solved the problems of internal semantic information loss and single feature extraction of traditional word vectors in named entity recognition in the field of automobile production equipment faults. Named entity recognition technology has achieved many good results in the fields of medical treatment, agriculture and electric power, helping people to extract key information with specific meaning from massive unstructured texts. Compared with the achievements of named entity recognition technology in other fields, the named entity recognition technology in the field of government audit is not only in its infancy but also faces many problems. In the field of auditing, there are few normative datasets, and traditional models cannot be fully trained, resulting in poor model recognition effect.