## 7 Conclusions  
(1) The Longitudinal Deformation Profile (LDP) characteristics were systematically analyzed for nine stratigraphic combinations across three distinct rock mass types. Homogeneous strata exhibited deformation patterns, characterized by an initial increase followed by gradual attenuation. Rock masses with a Geological Strength Index (_GSI_) of 15 demonstrated significantly higher deformation magnitudes. For cases involving stratigraphic interfaces, pre-interface deformation followed an increase-decrease trend, while post-interface deformation magnitudes were governed by the lithological parameters of the subsequent stratum. (2) A hybrid SE-CNN-LSTM-Attention neural network was proposed for predicting the surrounding rock properties ahead of tunnel faces. Utilizing LDP curves as input features, the model achieved classification accuracy rates of 93.25% on the training set and 91.20% on the test set, demonstrating robust predictive capability for stratigraphic characterization. (3) Comparative ablation studies against four benchmark architectures (SE-CNN-LSTM, CNN-LSTM-Attention, CNN-Attention, and baseline CNN) revealed significant performance enhancements. The proposed model improved the test set accuracy by 3.24%, 3.08%, 4.16%, and 6.48%, respectively, validating the efficacy of the synergistic integration of SENet channel attention, LSTM temporal modeling, and spatial-contextual attention mechanisms. Figure 11: Confusion matrices of training sets and test sets for each variant model in the ablation experiment. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} \\ \cline{3-8}  & & **Macro Avg** & **Weighted Avg** & **Macro Avg** & **Weighted Avg** & **Macro Avg** & **Weighted Avg** \\ \hline SE-CNN-LSTM-Attention & 91.20\% & 0.9114 & 0.9152 & 0.9074 & 0.9120 & 0.9080 & 0.9123 \\ (Baseline) & 87.96\% & 0.8854 & 0.8897 & 0.8762 & 0.8796 & 0.8732 & 0.8774 \\ \(\text{CNN-LSTM-Attention}\) & 88.12\% & 0.8793 & 0.8862 & 0.8785 & 0.8812 & 0.8755 & 0.8804 \\ \(\text{CNN-Attention}\) & 87.04\% & 0.8655 & 0.8724 & 0.8655 & 0.8704 & 0.8650 & 0.8709 \\ \(\text{CNN}\) & 84.72\% & 0.8426 & 0.8488 & 0.8455 & 0.8472 & 0.8472 & 0.8432 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison results of SE-CNN-LSTM-Attention with SE-CNN-LSTM, CNN-LSTM-Attention, CNN-Attention, and CNN on the test set. Notably, the current training data were exclusively derived from numerical simulations, with tunnel burial depth fixed as a constant. Future work will incorporate burial depth as an input variable and validate the model against field-monitored data from practical engineering projects. Conceptualization, Y.W. and H.F.; methodology, S.S., Y.W., H.F., Y.P., Y.L. and Z.L. ; software, S.S. and Z.Z. ; validation, R.S., Y.W., Y.P., Y.L. and Z.L. ; formal analysis, R.S. and Y.W. ; investigation, R.S., Y.W. and Z.Z. ; resources, Y.W. and H.F.; data curation, R.S., H.F. and Y.W. ; writing--original draft preparation, R.S. and S.S.; writing--review and editing, R.S. and S.S.; visualization, R.S. ; supervision, Z.Z. ; project administration, Y.P., Y.L. and Z.L. ; funding acquisition, Y.W. and H.F. All authors have read and agreed to the published version of the manuscript. This research was sponsored by the Hunan Provincial Communications Science Research Institute Co., Ltd., through the Science and Technology Project of Hunan Provincial Department of Transportation (202220). We also received support from the Science and Technology Project of Hubei Provincial Department of Transportation (2020-186-2-6) and the National Key Research and Development Program of China (2022 YFB2603301). Not applicable. The data are available from the corresponding author upon reasonable request. The authors give thanks to the editors and anonymous reviewers for their constructive comments.