## 4 Results and Discussion  
### Hyperparameter Adjustment for the Student's Knowledge Level Prediction  
Figure 3 shows iterative curves of the SVM model under different hyperparameter adjustment algorithms, including the SSA-SVM, ISSA-SVM, and KPCA-ISSA-SVM. With the increase in the number of iterations, fitness can be significantly reduced when the three hyperparameter adjustment algorithms are combined with the SVM model, indicating that all three hyperparameter tuning algorithms can effectively classify SVM machine learning models and predict students' knowledge level, which shows that all three algorithms are effective in classifying SVM models. Also, the mixed models of ISSA-SVM and KPCA-ISSA-SVM can significantly reduce fitness compared with SSA-SVM, which reflects their advantages. Among these, ISSA-SVM obtains the smallest fitness, at about 0.0245. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Actual} & Positive & True positive (TP) & False negative (FN) \\ \cline{2-4}  & Negative & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix. Figure 2: Kernel principal component analysis. ### Hyperparameter Adjustment for the Student's Knowledge Level Prediction  
Figure 4 gives the predicted UNS for the training and testing datasets using the SSA-SVM hybrid model. As mentioned earlier, 70% of the data in this study were used for the training set and 30% were used for the test set to predict the students' knowledge level predictions. It can be seen that when the training set and the test set are used to predict the students' knowledge level, their accuracy values are 96.0854% and 91.8033%, respectively. The accuracy of the training set is significantly higher than that of the test set, which may cause under-fitting in the prediction process of students' knowledge levels. When comparing four different models, as shown in Table 3, we see that the accuracy of the training set did not improve significantly. The data show that the prediction accuracy of the SSA-SVM model in the training set is higher than that of the ISSA-SVM model. However, by comparing the results of the test set, it can be seen that the SSA-SVM model displays an overfitting phenomenon in the training stage. That is, the results of the training set differ greatly from those of the test set. However, the test results show that with an increase in complexity, the test results become increasingly close to the results of the training set. The accuracy values of the ISSA-SVM model for the training and test sets are 95.7295% and 95.082%, respectively, and those of the KPCA-ISSA-SVM model are 96.7972% and 96.7213%, respectively. This suggests that additional algorithms and information processing methods can effectively solve the underfitting problem when predicting students' knowledge levels. Therefore, before the prediction of students' learning level, data processing methods or data search algorithms can be used to pre-process the initial dataset, in order to effectively improve the prediction accuracy of the model. This reduces forecast errors due to underfitting or overfitting [35; 36]. Figure 3: Iterative curves of the SVM model under different hyperparameter adjustment algorithms: (a) SSA-SVM; (b) ISSA-SVM; (c) KPCA-ISSA-SVM. Figure 4: Predicted UNS for the training datasets and testing datasets. ### Hyperparameter Adjustment for the Student's Knowledge Level Prediction  
To further analyze the model's ability to predict students' knowledge level, and evaluate the composite model's ability to improve the model, we performed analyses via a confusion matrix. The blue part of Figure 5 indicates that the predicted data match the actual data, while the brown part means that the predicted structure of the model deviates from the actual data, and does not compound the type of the actual data.