## 5 Results and Discussion
### Network Performance Evaluation Metrics  
The indicators obtained from the five tests are shown in Table 5.  
The evaluation of network performance metrics across different categories (N1, T1, F1, T2, F2, T3, and F3) underscores the model's classification capabilities and its consistency in handling various data types. Overall, the model demonstrated high accuracy across all categories, indicating robustness and reliability in classification tasks.  
For the N1 category, the model achieved the highest accuracy at 99.73%, with precision and recall rates of 98.94% and 98.17%, respectively. These metrics suggest that the model performs exceptionally well in this category, achieving a near-perfect balance between correctly identifying positive instances and minimizing false positives and negatives.  
In the T1 category, the model maintained a high accuracy of 99.69%, with precision and recall rates of 96.61% and 97.85%, respectively. The F1 score of 97.23% reflects strong performance, indicating the effective handling of this category with minimal errors.  
In both the F1 and T2 categories, the model exhibited an accuracy rate of 99.01%. In the F1 category, the model had a precision of 96.56% and a recall of 97.54%, while in the T2 category, a precision of 97.53% and a recall of 96.87% were achieved. These metrics suggest that, although the model performs well, there is slight variation in its ability to consistently identify true positives across these categories.  
The F2 category, with an accuracy of 98.84%, a precision of 95.35%, and a recall of 97.83%, indicates strong performance but highlights room for improvement in balancing precision and recall.  
In the T3 and F3 categories, the model exhibited relatively lower accuracy and precision/recall balances. In the T3 category, the model had an accuracy of 98.23%, with precision and recall rates of 95.73% and 93.99%, respectively, indicating higher rates of false positives and false negatives. Similarly, the F3 category, with an accuracy of 98.72%, a precision of 96.59%, and a recall of 95.59%, suggests that, although the model performs well, it could benefit from further optimization to enhance the balance between precision and recall.  
In summary, the model demonstrates robust classification performance across various categories, with N1 and T1 exhibiting the highest reliability. The consistency in high accuracy across all categories underscores the model's overall effectiveness. However, specific categories, such as T3 and F3, indicate areas for potential improvement to achieve a more balanced performance in precision and recall.  
The visual analysis of the two confusion matrices, as shown in Figure 13, reveals that the model performs remarkably in classifying most categories, exhibiting high classification accuracy. Specifically, Figure 13a shows the distribution of the classifier's results between the actual and predicted categories, with diagonal values representing correctly classified samples and off-diagonal values indicating misclassified samples. Figure 13b displays the percentage distribution of classification results, providing a more intuitive reflection of the model's classification accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & **TP** & **TN** & **FP** & **FN** & **Acc** & **Precision** & **Recall** & **F1 Score** \\ \hline N1 & 375 & 3742 & 7 & 4 & 99.73\% & 98.94\% &  
In the majority of categories, the model demonstrated high classification accuracy, where the performance was nearly perfect with almost no misclassification. However, there were still certain degrees of misclassification among some categories. This indicates that while the model possesses strong overall classification capabilities, there remains room for improvement in distinguishing specific categories.