## 4 Experimental Results and Analysis
### Performance Under Different Data Split Ratios  
In this section, we present the performance of the model under different data split ratios. As shown in Table 5, we designed three distinct data split strategies (80%:20%, 50%:50%, and 20%:80%). As the proportion of training data decreases, the classification difficulty of the test data increases accordingly. Experimental results indicate that our CNN-LSTM method achieves the best performance across all data split ratios. It is worth noting that even when the training data constitute only 20% of the total dataset, our network still attained a classification accuracy of 71.87%, demonstrating that our network can effectively  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**CNN**} & \multirow{2}{*}{**LSTM**} & \multicolumn{4}{c}{**Test Data: 20\% EXP Data**} & \multicolumn{2}{c}{**Metrics: ACC (\%) \(\uparrow\)**} & \multicolumn{2}{c}{**Avg\_FOM \(\uparrow\)**} \\ \cline{3-8}  & & **H** & **B** & **C** & **O** & **AL** & **Overall** \\ \cline{2-8}  & & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **Avg\_FOM** \\ \hline ✓ & & 88.52 & 65.57 & 86.89 & 68.85 & 98.28 & 81.46 & 0.44 \\  & ✓ & 68.86 & 67.21 & 73.77 & **91.80** & 72.41 & 74.83 & 0.39 \\ ✓ & ✓ & **98.36** & **81.97** & **88.52** & 90.16 & **100.00** & **91.72** & **0.90** \\ \hline \hline \end{tabular} The best results are bolded and underlined.  
\end{table}
Table 4: The experimental results for analyzing the effectiveness of the CNN and LSTM modules.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Data**} & \multirow{2}{*}{**Test Data**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Metrics: ACC (\%) \(\uparrow\)**} & \multicolumn{2}{c}{**Avg\_FOM \(\uparrow\)**} \\ \cline{3-8}  & & & **H** & **B** & & & & **Overall** \\ \cline{3-8}  & & & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **Avg\_FOM** \\ \hline \multirow{3}{*}{H\({}_{1.5,\,\text{2 MeV}}\) + B,C,O,AL\({}_{2.5,\,\text{375 MeV}}\)} & \multirow{3}{*}{H\({}_{2.5\,\text{MeV}}\)} & \multirow{3}{*}{H\({}_{2.5\,\text{MeV}}\) + B,C,O,AL\({}_{5\,\text{3 MV}}\)} & SVM-MV & 72.38 & 0.00 & 0.00 & 22.00 & 16.93 & 0.20 \\  & & LSTM & **100.00** & **47.60** & 0.00 & 0.00 & 33.00 & 35.91 & 0.39 \\  & & CNN-LSTM & 21.00 & 10.00 & **96.00** & **28.71** & **100.00** & **51.42** & **0.55** \\ \hline \multirow{3}{*}{H\({}_{2.5\,\text{MeV}}\) + B,C,O,AL\({}_{2.5\,\text{MeV}}\)} & \multirow{3}{*}{H\({}_{1.5\,\text{MeV}}\) + B,C,O,AL\({}_{2.5\,\text{MeV}}\)} & SVM-MV & 79.21 & 0.00 & **97.00** & 0.00 & **97.73** & 53.67 & 0.28 \\  & & LSTM & 71.00 & 0.99 & 2.97 & 0.00 & 90.91 & 51.63 & 0.18 \\  & & CNN-LSTM & **91.00** & **55.45** & 40.59 & **54.00** & 72.73 & **62.45** & **0.51** \\ \hline \multirow{3}{*}{H\({}_{1.5,\text{25 MeV}}\) + B,C,O,AL\({}_{2.5,\text{MeV}}\)} & SVM-MV & **100.00** & 98.00 & 49.00 & 0.00 & 72.20 & 63.84 & 0.46 \\  & & LSTM & 75.00 & 63.2 & 0.00 & 2.00 & 91.00 & 45.91 & 0.23 \\ \cline{1-1}  & & CNN-LSTM & 43.00 & **99.00** & **77.00** & **51.00** & **98.00** & **73.60** & **0.71** \\ \hline \hline \end{tabular} The best results are bolded and underlined.  
\end{table}
Table 3: The comparison experiment results of previous methods and our CNN-LSTM model under test on particles with unseen energy level scenario.  
extract discriminative features even with limited training data. This highlights that our proposed CNN-LSTM network demonstrates significant potential for practical applications.