## 1 Introduction  
In recent years, with the widespread promotion of medical information systems in China's hospitals, a large amount of electronic medical text data has been generated as a result [1]. It is especially important to identify and integrate the huge amount of medical text data efficiently and accurately. This is of great practical significance for building professional medical knowledge graphs, conducting medical knowledge reasoning, and providing accurate medical services [2]. As medical entities are important carriers of medical information, efficient and accurate identification of the entities contained in medical texts is the focus of the research [3].  
For the task of Chinese medical entity recognition, one of the most important problems encountered so far is the lack of labeling data. The tagged data can be obtained by processing the Chinese medical text. First, the Chinese medical text needs to be desensitized to remove some private information. Then, the medical text needs to be annotated. The medical field is a highly specialized field. Marking of medical texts requires professional medical staff after uniform training and obtaining the appropriate standards [4, 5]. However, the work intensity of medical personnel is relatively high, and the annotation of medical data consumes a lot of resources. This increases the difficulty of medical data annotation, so it causes the problem of insufficient marked medical text data. In Chinese medical entity recognition tasks, the problem of low model recognition performance due to insufficient annotation data can occur, which seriously hinders the development of Chinese medical entity recognition.  
To address the problem of poor model recognition due to insufficient labeled data in Chinese medical entity recognition tasks, this paper proposes a Chinese medical entity recognition model based on multi-neural network fusion and the improved Tri-Training algorithm. The model performs semi-supervised learning via the Tri-Training algorithm, which primarily uses a large unlabeled corpus to augment the labeled dataset to train a medical entity recognition model with superior performance. In addition, the BERT-IDCNN-BiLSTM-CRF (BIBC) model is proposed in this paper as a base classifier for entity recognition. The BIBC model performs entity recognition by fusing multiple neural networks, adding an IDCNN layer to the classical BiLSTM-CRF to focus more on the local information of the text. Further, the model uses the pre-trained model BERT to better represent the semantic information. However, during the training process, if the initial base classifier is mislabeled, the error will gradually amplify, causing error accumulation. When the error intensifies, the performance of the semi-supervised learning model also decreases. In order to reduce this error rate, this paper improves the Tri-Training algorithm in terms of the division of the initial sub-training set, the network model selection of the base classifier, and the voting method of ensemble learning according to the characteristics of the Chinese medical entity recognition task. Comparative experiments are conducted to verify the effects of the introduction of semi-supervised learning and the improvement of the Tri-Training algorithm on the model performance.