## 3 Results
### Comparison of Classification Models  
Here, we compare the distributed learning classification module (DLCM) proposed in the article with several classification models commonly used in Raman spectroscopy classification tasks (SVM, PCA + LR, RF). And, in order to explore whether our RSEM and RSDM have improvement effects on other classification models, we set up three sets of comparative experiments. All models in the first group of experiments were trained using unprocessed spectral data. In the second group of experiments, all models were trained using data-enhanced data. In the third group of experiments, all models were trained using data-enhanced and denoised data. As shown in Figure 16, our proposed classification model performed the best in both the second and third experiments. In the first set of experiments, the classification accuracy of the model we proposed was lower than that of the SVM model, because the dataset size was small, and the deep learning model could not play a role. Further analyzing the experimental results, we found that  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Choice** & **RSEM** & **RSDM** & **Pathogen** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{1} & \multicolumn{4}{c}{_Vibrio parahaemolyticus_} & \multirow{4}{*}{0.9081} & 0.913 & 0.9106 \\  & & & _Escherichia coli_ & & 0.8920 & 0.8771 & 0.8845 \\  & & & _Aerononas hydrophilia_ & & 0.9195 & 0.9249 & 0.9222 \\  & & & _Aerononas veronii_ & & 0.907 & 0.9123 & 0.9096 \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{\(\checkmark\)} & \multicolumn{4}{c}{_Vibrio parahaemolyticus_} & \multirow{4}{*}{0.9444} & 0.977 & 0.9605 \\  & & & _Escherichia coli_ & & 0.9636 & 0.9464 & 0.955 \\  & & & _Aerononas hydrophilia_ & & 0.9643 & 0.9818 & 0.973 \\  & & & _Aerononas veronii_ & & 0.9455 & 0.8966 & 0.9204 \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{\(\checkmark\)} & \multicolumn{4}{c}{_Vibrio parahaemolyticus_} & \multirow{4}{*}{0.9322} & 0.9565 & 0.9442 \\  & & & _Escherichia coli_ & & 0.9510 & 0.9327 & 0.9417 \\  & & & _Aerononas hydrophilia_ & & 0.9519 & 0.9706 & 0.9612 \\  & & & _Aerononas veronii_ & & 0.9400 & 0.9126 & 0.9261 \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{\(\checkmark\)} & \multicolumn{4}{c}{_Vibrio parahaemolyticus_} & \multirow{4}{*}{0.9824} & 0.9929 & 0.9876 \\  & & & _Escherichia coli_ & & 0.9931 & 0.9897 & 0.9914 \\ \cline{1-1}  & & & _Aerononas hydrophilia_ & & 0.9932 & 0.9966 & 0.9949 \\ \cline{1-1}  & & & _Aerononas veronii_ & & 0.9892 & 0.9786 & 0.9839 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the overall model ablation experiment.  
after the RSEM and RSDM, the accuracy of several classification models has been greatly improved, which proves that our proposed network can also be applied to the performance improvement of traditional machine learning classification. Therefore, we believe that our proposed network has definite potential in solving other microbial Raman spectroscopic classification tasks.  
Additionally, to test the classification performance of our model in the new dataset, we obtained samples of the four pathogenic bacteria mentioned in the manuscript from another batch of shrimp, and Raman spectroscopy datasets of four pathogenic bacteria were made based on the samples. We used the trained model to make direct predictions on the dataset, as shown in Table 6. Our model achieved a classification accuracy of 95.8% on the untrained new dataset, which also proves that our model has practical significance.