## 4 Experimental Results and Analysis
### Analysis of Results  
**Experiment 1**. : _In order to verify the effectiveness of the data enhancement method used in this paper, the textbook text and the original text after data enhancement were input into the TextCNN text classifier and evaluated by the F1-measure; the experimental results are shown in Figure 6._  
**Experiment 2**. : _Because different hyperparameter settings have different effects on the model, the parameters need to be optimized._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Our Method** & **IoMET** & **Bi\_LSTM** & **Bi\_GRU** & **GRNN** \\ \hline Convolution & & & & & \\ kernel size & (2, 3, 4) & (2, 3, 4) & - & - & - \\ \hline Regularization & L2 (0.001) & L2 (0.001) & - & - & L2 (0.001) \\ \hline Drop out & (0.1) & 0.5 & 0.2 & 0.2 & 0.2 \\ \hline Optimization & Adam & Adam & SGD & SGD & Adam \\ method & Adam & Adam & SGD & SGD & Adam \\ \hline Batch size & 128 & 128 & 256 & 256 & 128 \\ \hline Epoch & 10 & 10 & 10 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model hyperparameter settings. Figure 6: Comparison of F1-measure curves for each category. The word vector matrices generated by different word vector dimensions are different, and the text feature information expressed is also different. In order to find the optimal word vector dimension under the Shanghai textbook text dataset, multiple groups of control experiments are set, and F1-measure is used as the model performance evaluation index. The performance of the model in different dimensions is shown in Figure 7.  
Model overfitting often occurs in the process of model training. Because the loss function of overfitting the model in the test set and the training set is very different, the overfitting model cannot be used in practical applications. [PERSON] et al. [23] proposed that in the process of training the model some hidden layer nodes can be ignored in each training block in order to reduce the interaction of these hidden layer nodes, so as to reduce the occurrence of overfitting. Therefore, in this paper, multi-group control experiments were carried out in the (0, 1) interval to observe the classification performance of the model under different dropout values. The performance of the model under different dropout values is shown in Figure 8.  
Figure 8: Effect of different dropout values on model performance. Figure 7: Effect of word vector dimension on model performance. For Q1, it can be seen from Figure 6 that the F1-measure values of each category of the textbook text after data enhancement are higher than those of the original text, verifying the effectiveness of the data enhancement method used in this paper, which can effectively solve the problem of unbalanced text datasets. For Q2, it can be seen from Figure 7 that the best classification performance of this paper is between 100 and 150 word vectors, which may be due to the decrease in the quality of the pre-trained word vectors from the corpus when the word vector dimension is set too high. From Figure 8, we can see that there is no significant change in the dropout value (0, 0.5), and that the best classification performance is achieved when dropout = 0.3. For Q3, it can be seen from Table 7 that, in the task of classifying the morphological indicators of college textbooks, the method proposed in this paper achieves the highest F1-measure value of 77.51%, with the word vector dimension of 150 dimensions and dropout = 0.3. The reference models obtain 61.28% (IoMET), 75.62% (Bi_LSTM), 74.82% (Bi_G RU), 75.19% (GRNN), and 78.35% (BERT) of the F1-measure value. The F1-measure values of BERT are less than 1% higher than those of this paper and the convergence speed is much slower than that of this paper because of the larger number of parameters to be trained. The original IoMET model cannot be used as a model, as it does not make good use of the linkage and location information between words, so when the input utterance is too long, some important semantic information may be lost.