## 5 A Prediction Model for Rock Mass Classification Ahead of the Driving Face
### Results and Analysis  
(1) Analysis of Model Loss Function  
The training and validation loss curves, along with accuracy metrics, are shown in Figure 9. The results indicate that both training and validation losses converge to low and comparable values, with no evident signs of overfitting or underfitting observed. This demonstrates the robust overall training performance of the model.  
(2) Analysis of Model Evaluation Metrics  
The key metrics for the machine learning model evaluation typically included precision, recall, F1-Score, and support. To comprehensively assess the hybrid SE-CNN-LSTM-Attention architecture proposed in this study, the model's performance metrics on the training and test sets are statistically summarized in Tables 9 and 10.  
Figure 8: Scatter plots of datasets by category.  
Figure 9: Loss functions of the model’s training set and validation set.  
As shown in the table, except for the relatively low data in Class 5 of the test set, the remaining data are generally between 0.8 and 1. This indicates that the model has good performance and high accuracy. Finally, the accuracy of the model on the training set is 93.25%, and the accuracy on the test set is 91.20%. From the confusion matrix in Figure 10, it can be observed that when the true label is from Class 5, the predicted label sometimes appears as Class 2. The reason for this is that after data augmentation, since mirror flipping operations occur along the \(x\)-axis in basic augmentation alongside operations that randomly increase and decrease the amplitude in both basic and composite augmentations, the augmented data of Class 2 and Class 5 show highly similar features. Therefore, further research is needed to improve the performance of the model when predicting Class 5 data.  
(3) Confusion Matrix  
The classification confusion matrix of the model is illustrated in Figure 10. The results demonstrate high predictive accuracy in determining the grade of the surrounding rock ahead of the tunnel face based on the LDP (Load Deformation Profile) curve characteristics, with consistent performance across most rock mass classifications.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Formation Classification** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0 & 0.9273 & 0.7969 & 0.8571 & 64 \\
1 & 0.9881 & 0.9326 & 0.9565 & 89 \\
2 & 0.7317 & 0.8108 & 0.7962 & 74 \\
3 & 1.0000 & 1.0000 & 1.0000 & 73 \\
4 & 0.8272 & 0.9437 & 0.8816 & 71 \\
5 & 0.7288 & 0.6825 & 0.7049 & 63 \\
6 & 1.0000 & 1.0000 & 1.0000 & 83 \\
7 & 1.0000 & 1.0000 & 1.0000 & 68 \\
8 & 1.0000 & 1.0000 & 1.0000 & 63 \\ Macro avg & 0.9114 & 0.9074 & 0.9080 & 648 \\ Weighted avg & 0.9152 & 0.9120 & 0.9123 & 648 \\ Accuracy & & 0.9120 & & 648 \\ \hline \end{tabular}
\end{table}
Table 10: Statistical table of evaluation indicators for the test set of the SE-CNN-LSTM-Attention model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Formation Classification** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
0 & 0.8590 & 0.7614 & 0.8072 & 176 \\
1 & 1.0000 & 0.9868 & 0.9933 & 151 \\
2 & 0.9006 & 0.8735 & 0.8869 & 166 \\
3 & 1.0000 & 1.0000 & 1.0000 & 167 \\
4 & 0.7778 & 0.8698 &