## 4 Experimental Results and Analysis
### Visualization of the Feature Distribution  
In this section, we apply Principal Component Analysis (PCA) [23] to reduce the dimensionality of the feature vectors of the test data to three dimensions for visualization purposes. As shown in Figure 6, we present the 3D visualization of the feature vectors of particle samples obtained by the SVM-ovr, LSTM, and our CNN-LSTM networks. It can be observed that the sample points of various types of particles in SVM-ovr are dispersed  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Test Data: 20\% EXP Data**} & **Metrics: ACC (\%) \(\uparrow\)** & **Avg\_FOM \(\uparrow\)** & **F1\_Score \(\uparrow\)** \\ \cline{2-9}
**Num\_CNN** & **Num\_LSTM** & **H** & **B** & **C** & **O** & **AL** & **Overall** & \\ \cline{3-9}  & & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **Fl\_Score** & **Avg\_FOM** \\ \hline
1 & 1 & 85.25 & 80.33 & 73.77 & 80.33 & 96.55 & 83.11 & 0.8340 & 0.26 \\
1 & 2 & 93.44 & **83.61** & 81.97 & 77.05 & **100.00** & 87.09 & 0.8720 & 0.69 \\
1 & 3 & 85.25 & 81.97 & 85.25 & 81.97 & 98.28 & 86.42 & 0.8664 & 0.68 \\
2 & 1 & 96.72 & 73.77 & 81.97 & 70.49 & **100.00** & 84.44 & 0.8467 & 0.48 \\
2 & 2 & 98.36 & 81.97 & **88.52** & **90.16** & **100.00** & **91.72** & **0.9179** & **0.90** \\
2 & 3 & **100.00** & 72.13 & 65.57 & **44.26** & **100.00** & 76.16 & 0.7538 & 0.55 \\
3 & 1 & 93.44 & 86.89 & 83.61 & 78.69 & **100.00** & 88.41 & 0.8847 & 0.81 \\
3 & 2 & 88.52 & 83.61 & 77.05 & 80.33 & **100.00** & 85.76 & 0.8592 & 0.60 \\
3 & 3 & 95.08 & 70.49 & 57.38 & 54.10 & **100.00** & 75.17 & 0.7594 & 0.53 \\ \hline \hline \end{tabular}  
* The best results are bolded and underlined.  
\end{table}
Table 6: The grid search results for analyzing the selection of the number of layers.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multicolumn{3}{c}{**EXP Data Split Ratio**} & \multicolumn{5}{c}{**Metrics: ACC (\%) \(\uparrow\)**} & **Avg\_FOM \(\uparrow\)** & \\ \cline{2-9}  & \multirow{2}{*}{**Training**} & **Test** & **H** & **B** & **C** & **O** & **AL** & **Overall** \\ \cline{3-9}  & & & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **ACC** & **Avg\_FOM** \\ \hline SVM-ovr & & & 60.66 & 55.74 & 55.74 & 85.25 & 56.90 & 62.91 & 0.16 \\ LSTM & 80\% & 20\% & 68.86 & 67.21 & 73.77 & **91.80** & 72.41 & 74.83 & 0.40 \\ CNN-LSTM & & & **98.36** & **81.97** & **88.52** & 90.16 & **100.00** & **91.72** & **0.90** \\ \hline SVM-ovr & & & 61.18 & 53.64 & 57.62 & 79.47 & 55.56 & 61.55 & 0.18 \\ LSTM & 50\% & 50\% & 53.29 & 66.23 & 72.19 & **84.77** & 82.64 & 71.70 & 0.27 \\ CNN-LSTM & & & **91.45** & **81.46** & **88.74** & **84.77** & **100.00** & **89.19** & **0.55** \\ \hline SVM-ovr & & & 48.39 & 41.94 & **70.97** & **80.65** & 55.17 & 59.48 & 0.15 \\ LSTM & 20\% & 80\% & 58.44 & 65.29 & 56.85 & 78.01 & 77.06 & 67.03 & 0.33 \\ CNN-LSTM & & & **81.89** & **66.53** & 51.45 & 61.41 & **99.13** & **71.87** & **0.45** \\ \hline \hline \end{tabular}  
* The best results are bolded and underlined.  
\end{table}
Table 5: The experimental results under different data split ratios.  
across the feature space, indicating poor separability. In contrast, in the LSTM network, the cohesion of samples belonging to the same type of particle is improved, but the separability between different particle categories remains relatively weak. In comparison, our proposed CNN-LSTM network not only forms tighter clusters for samples of the same type of particles, but also enhances the separability between different types of particles. This phenomenon demonstrates that our proposed CNN-LSTM network can effectively extract features with stronger separability and generalization capabilities, thereby enhancing the classification performance.