## 4 Empirical Studies
### Prompt-BERT Model Classification Effect  
1. Parameter selection  
The BERT pre-training model used in this study is based on Google's Bert-base-chinese version, which performs well in Chinese natural language processing tasks. In order to fully utilize the feature capturing ability of the BERT model, the parameters are set as follows. The batch size is set to 128, which ensures the stability of training and improves efficiency at the same time. The pad size is set to 64, which can adapt to the length of most short medical texts. The learning rate is set to 5 \(\times\) 10\({}^{-5}\) to ensure the convergence of the model training. The hidden size is kept at 768, which is consistent with the original model of BERT and makes full use of its semantic expression capability. The convolutional kernel size is set to (3,4,5) to capture local features of different lengths, and the configuration of 256 convolutional kernels (num_filters) enhances the feature extraction capability. In order to prevent overfitting, the dropout is set to 0.1. For the recurrent neural network part, the rnn_hidden is set to 256, and a two-layer RNN structure is used to strengthen the capture of text sequence information. The early stopping condition is set in training; if the model performance is not improved within 300 batches, the training will be stopped early to avoid the waste of resources and the risk of overfitting. Meanwhile, a self-adaptive attention mechanism is introduced into the model, which assigns weights to each position in the input sequence by calculating its representation vector to determine the key information. Specifically, the input sequence is passed through a linear layer to generate query vectors, key vectors, and value vectors, which are used to calculate the attention score, and then the input sequence is weighted according to the score to generate context-related vectors, so as to realize focusing on key information. 2. Comparison of classifier classification effects  
After determining the parameter configurations, the Prompt-BERT-RCNN model is applied to evaluate the classification effect of the extended short medical text and compare it with other classifiers for ablation experiments. The data of each classifier are converted into embedding data by the bag-of-words model or BERT model. The specific data input steps are as follows:  
FCNN: The embedding data are fed into the dropout layer and shallow fully connected layer for classification output. DNN: The embedding data are fed into the dropout layer and deep fully connected layer for classification output. TextCNN (Text Convolutional Neural Network): The embedding data are passed into the convolutional layer, which uses 128 convolutional kernels, and each convolutional kernel is of five different sizes, followed by passing the output of the convolutional layer to the maximum pooling layer and then finally classifying the output through the dropout layer and the shallow fully connected layer. BERT: The pre-trained BERT model feeds the embedding data into the dropout layer and shallow fully connected layer for classification output. BERT-RNN: Firstly, the embedding data are fed into a bidirectional LSTM network through a pre-trained BERT model to capture the sequence information. Finally, the classification output is performed by dropout layer and shallow fully connected layer. BERT-RCNN: The embedding data are first fed into a bidirectional LSTM network through a pre-trained BERT model to capture sequence information. Then, feature extraction is performed through the convolutional layer and passed into the maximum pooling layer, and finally classification output is performed through the dropout layer and shallow fully connected layer.