## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.4.2 Comparison Experiments of Different Improved Tri-Training Algorithm Models  
First, the experiments are conducted to compare the effect of the introduction of the semi-supervised learning Tri-Training algorithm on the model performance. The results are shown in Figure 4, where the accuracy, recall, and F1 values of Model 2 with semi-supervised learning are improved compared to Model 1 with supervised learning. This is because the semi-supervised learning approach introduces a large amount of unlabeled data and increases the number of labeled training data samples by selectively expanding the labeled sample set. By adding semi-supervised learning, Model 2 can solve the problem of having only a small amount of labeled data in the Chinese medical dataset, thus improving the generalization ability of the model. The experiments verify that the semi-supervised learning approach can improve the performance of Chinese medical entity recognition models. We next test whether the improvement of the Tri-Training algorithm in this paper improves the performance of the model, and the models used in this group of comparison experiments are Model 2, Model 3, and Model 4 mentioned above. The experimental results are shown in Figure 5, where Model 3 has improved recall and F1 values compared with Model 2. This is because Model 3 changes the way of selecting the labeled sub-training set and the voting rule of the final integrated learning in the Tri-Training algorithm compared to Model 2. The use of an evenly distributed selection of sample sets prevents the waste of labeled sample data on the one hand and makes full use of the labeled sample sets on the other. This method can use three sample subsets with no overlap at all for training and better differentiate the base classifier model, thus improving the performance of the final obtained classifier model. Compared with Model 3, the biggest change in Model 4 is to change the structure of the three base classifiers. The three base classifiers in Model 4 are different from each other, which can avoid the problem that the three same classifiers also give wrong answers for the same sample instance. Three different base classifiers can avoid this problem to a certain extent, thereby improving the performance of the final  
Figure 4: The performance statistics of the semi-supervised learning model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision (P)** & **Recall (R)** & **F1** \\ \hline IC & 78.45 & 77.62 & 78.03 \\ BIC & 82.36 & 83.65 & 83.00 \\ BC & 81.85 & 80.23 & 81.03 \\ BBC & 83.52 & 84.79 & 84.15 \\ BIBC & 85.29 & 85.24 & 85.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of BIBC model and different baseline model results (Unit: %). model. It can be seen from the experimental data that the model proposed in this paper, compared with the entity recognition model of the original Tri-Training algorithm, has a certain degree of improvement in the three evaluation indicators of accuracy rate, recall rate, and F1 value, which proves that this paper's improvement to the model is effective. The Chinese medical entity recognition model based on semi-supervised learning proposed in this paper has the results of entity recognition for each type, as shown in Figure 6. The recognition effect of six types of medical entities is improved relative to the supervised learning model. Among them, the F1 value of anatomical sites is 92.71%, which is much higher than the remaining entities. The reason for this result is that the entity boundaries of anatomical sites are generally clearer and do not have particularly indistinguishable boundaries. Another reason is that the number of medical entities in anatomical sites is also a bit higher in comparison, and the combination of the two reasons makes its recognition result leading among the six categories. As shown in Table 4, we compare the experimental results of the different improved Tri-Training algorithm models proposed in this paper with other models for comparison.