## 1 Introduction  
With the establishment of China's smart government, the digitization of audit in smart government has witnessed rapid development, leading to a significant increase in the volume of audit data. However, faced with this ever-growing amount of audit information, auditors' efficiency and quality gradually fail to meet the demands of audit tasks [1]. The presence of numerous unstructured data can result in misjudgment and omission by auditors, thereby compromising the reliability and accuracy of audit results. In order to facilitate information comparison for auditors during their tasks, it is crucial to identify named entities from unstructured data. Named Entity Recognition (NER), as a prominent research focus within the natural language processing field, primarily aims at identifying target entities with specific meanings within particular domains from unstructured text [2]. For government audits specifically, NER plays a vital role in identifying relevant entities, such as project names, addresses, amounts involved, timeframes, etc., from audit texts. Named entity recognition technology originated from the MCU-6 conference in 1995 [3], which first proposed the concept of named entity recognition and made named entity recognition a basic task in natural language processing. After years of development, named entity recognition technology has evolved from the method that initially relies on rules and dictionaries to the method that relies on traditional machine learning models and then to the method that is based on various deep learning technologies. The early rule-and-dictionary-based methods manually construct rule templates that conform to such texts by combining the language features of the text with relevant dictionaries and finally use manual matching methods to identify entities related to the rule templates. There are disadvantages that require much manpower, poor portability, low reusability, and dependence on rule templates. With the help of traditional machine learning models, researchers regard named entity recognition as a sequence annotation task, and the most common are the hidden Markov model [4], conditional random field model [5; 6] and maximum entropy model [7]. [PERSON] et al. [8] improved the traditional hidden Markov model, not only considering the characteristics of the word itself, but also considering the impact of N event states before and after the word, improving the accuracy of model recognition. Although traditional machine learning alleviates the early over-reliance on template rules to some extent, the improvement of model accuracy requires a large amount of feature labeled data to support. The powerful feature extraction ability of deep neural network does not require feature engineering for text. In recent years, deep learning-based methods have become a popular research direction in named entity recognition tasks [9]. [PERSON] et al. [10] combined convolutional neural network and conditional random field models to construct a general neural network framework to learn the internal features of a large number of unlabeled data to achieve named entity recognition. The short-term memory effect of traditional convolutional neural network and recurrent neural network is good, but it cannot record the pre- and post-sequence feature information with a long distance, which will lead to gradient attenuation. The works of [11; 12] first proposed the model framework combining long short-term memory network, bidirectional long short-term memory network and conditional random field, which alleviates gradient explosion and attenuation by obtaining bidirectional feature information of word vectors. It is applied to named entity recognition with excellent recognition ability and has become the basic framework of most model feature extraction layers and widely used. [PERSON] et al.