## 2 Related Work  
In 2015, Huang et al. [18] proposed the classical BiLSTM-CRF sequence annotation model, which uses BiLSTM to extract the sentence distant contextual information, combined with CRF to consider tag dependencies, and obtained the best results at that time. However, since Chinese characters do not have clear separators between them and face difficulties in word separation, the performance of using the classical model directly on Chinese datasets will be deviated. Thus, many scholars in China have optimized and adjusted the model for the characteristics of Chinese datasets. Zhang et al. [19] designed a Lattice LSTM network structure that combines information about the characters themselves and the words they belong to to mitigate the effects of incorrect word separation. To make full use of the information in the text, Qiang et al. [20] also proposed to add lexical features to enhance the performance of the neural network model. Other scholars proposed to use convolutional neural networks [21] for the task of Chinese entity recognition, using CNNs to extract local features of sequences to solve the impact of semantic deficiencies caused by unseparated Chinese data. Chiu et al. [13] proposed to use a combination of BiLSTM and CNN models, using CNNs to learn fine-grained features in characters and BiLSTMs to complete the task of sequence annotation. A combined model is proposed by combining the advantages of the models. Ding et al. [22] used graph neural networks to model the entity recognition task and used external lexicons for feature supplementation during the training process to learn the features inside automatically using the features of the model. Chiu [13] et al. argue that a dilated convolution network can improve the speed in training and prediction by overlaying CNNs, which can expand the perceptual field of the model. In addition, Strubell et al. [23] propose an Iterated Dilated Convolutional Neural Network (IDCNN), which makes remarkable improvement in speed and computationally efficiency with a SOTA-level accuracy in an NER task. However, these studies do not consider in an integrated way that fusing models together can capture more complete global and local features. In recent years, Chinese entity recognition models have been gradually applied to the biomedical field. Entities in the biomedical domain are more specialized than those in other domains for Chinese recognition tasks, and there are a large number of entities with a mixture of numbers, Chinese, English, and symbols, e.g., medication dose entity: '500 mg/d'; the entity of the test indicator: '>12.4 mmol/L', etc. The existence of these entities makes the identification of Chinese named entities in the medical field extremely difficult. To address these difficulties, a number of medical NER models have been investigated. In 2019, Zhang et al. [24] pre-trained BERT and used the embedding as the input feature of BiLSTM-CRF to solve the problem of medical NER for breast cancer. Li et al. [25] combined the attention mechanism and BiLSTM for entity extraction from Chinese electronic medical records. The method captured more contextual information about the entities through attention and further improved the model recognition performance using features such as medical lexicon and lexical properties. Ji et al. [26] added an entity auto-correction algorithm to the attention-Bi-LSTM-CRF model to correct the recognition of entities using historical entity information. In 2020, Li et al. [27] pre-trained the BERT model using untagged clinical text based on the clinical NER datasets of CCKS-2017 and CCKS-2018, and further improved the model performance using the introduction of dictionary features and root features of Chinese characters. In 2021, Li et al. [28] used CRF to specify identification rules in the identification of named entities in Chinese electronic medical records, solving the problem of ambiguous classification boundaries. Zhou et al.