## 1 Introduction  
Named entity recognition aims to identify entities such as names, places and organizations from unstructured texts. As the basic task of information extraction, NER will directly affect the downstream tasks such as relation extraction [1], entity linking [2] and the question answering system [3]. NER is often treated as a sequence labeling task; that is, each unit in the text is labeled with a predefined entity label [4]. In recent years, NER models based on deep learning have shown outstanding performance and gradually become the mainstream method [5]. Compared with English, Chinese texts lack natural word separators and explicit word boundaries. Therefore, Chinese NER needs to recognize the boundaries and types of entities at the same time, which is more difficult than English NER [6]. One way to implement Chinese NER is to segment sentences into word sequences first, and then recognize entities based on word sequences. However, the recognition effect of this method is vulnerable to the negative impact of segmentation errors. In order to avoid entity prediction failure caused by word segmentation errors, Chinese NER tasks generally model based on characters. In English, words with the same prefix or suffix can often be classified into one category. For example, words such as geologist, pianist and physicist have the suffix \"-ist\",which can represent entities engaged in a certain occupation. [PERSON] and [PERSON] et al. [7; 8] used a convolutional neural network (CNN) to capture the spelling features of prefixes and suffixes in English words; experiments showed that these spelling features can enhance the model's understanding of word semantics and improve the recognition performance of the NER model. Large-scale pre-trained language models, such as BERT [9] and GPT [10], use the word-piece method to split a word into multiple pieces, which can effectively express out-of-vocabulary words while reducing the size of the vocabulary, thus improving the semantic representation ability of the model. Different from English, Chinese characters are ideographic characters and have no prefix, suffix, root and other characteristics of English words. Chinese characters evolved from hieroglyphs, and their glyph structure contains rich semantic information. For example, \"\(\mathcal{T}\) (flower)\", \"\(\mathcal{T}\) (lotus)\" and \"\(\mathcal{T}\) (tea)\" all contain the radical \"\(\mathcal{\cdots}\) (grass)\", indicating that these three characters are related to herbaceous plants. This means that when there are characters out-of-vocabulary, we can deduce the approximate semantics of out-of-vocabulary characters from the glyph features of Chinese characters, such as radicals. At the same time, in Chinese naming culture, it is also preferred to use radicals with Wu-Xing (five elements) or positive meanings as part of the name. For example, radicals such as \"\(\mathcal{\times}\) (fire)\" and \"\(\mathcal{\mathbb{H}}\) (sum)\" often appear in names, while radicals such as \"\(\mathcal{\mathbb{H}}\)\" (epilepsy)\", which represent diseases, rarely appear in names. These patterns may be useful for identifying the Chinese names of people and businesses. Moreover, Chinese characters are mostly polysemous, meaning that one Chinese character has multiple meanings, and most of them need to be combined with other Chinese characters into words to have a more clear semantic representation. For example, the Chinese character \"\(\mathcal{\mathbb{H}}\)\" in \"\(\mathcal{\mathbb{H}}\) (bank)\" and \"\(\mathcal{\mathbb{H}}\) (crawling)\" have completely different semantics. In \"\(\mathcal{\mathbb{H}}\)\(\mathcal{\mathbb{H}}\)\(\mathcal{\mathbb{T}}\)\", \"\(\mathcal{\mathbb{T}}\)\" is a noun, indicating an institution, while in \"\(\mathcal{\mathbb{H}}\)\(\mathcal{\mathbb{H}}\)\(\mathcal{\mathbb{T}}\)\", \"\(\mathcal{\mathbb{T}}\)\" is a verb, indicating walking.