## 3 Result  
The results gathered for these frameworks are shown in Table 8. In terms of accuracy, the accuracy of the original CNN-BiLSTM without any attention mechanism was 0.865. For the frameworks with attention, the CNN-BiLSTM with CBAM, the CNN-attention-based BiLSTM with CBAM, and the CNN-attention-based BiLSTM with a non-local neural network exhibited worse performance than CNN-BiLSTM. The CNN-attention-based BiLSTM obtained the highest accuracy, 0.868, which was an improvement over CNN-BiLSTM. \begin{table}
\begin{tabular}{l l} \hline \hline
**CNN** & \\ \hline Convolutional layer & Filter = 32, kernel size = 200, stride = 8 \\ Rectified Linear Unit & \\ Batch normalization + dropout (0.70) & \\ Rectified Linear Unit & \\ Batch normalization + dropout (0.70) & \\ Max-pooling & \\ Max-pooling & Pool size = 8, stride = 8 \\ Non-local-neural network & F = 100 (1/2*Kernel size) \\ First Attention-Based BiLSTM & \\ Second Attention-Based BiLSTM & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The CNN (Convolutional Neural Networks)-attention-based BiLSTM (Bi-directional Long Short-Term Memory) with CBAM (Channel attention module). \begin{table}
\begin{tabular}{l l} \hline \hline
**CNN** & \\ \hline Convolutional layer & Filter = 32, kernel size = 200, stride = 8 \\ Rectified Linear Unit & \\ Batch normalization + dropout (0.70) & \\ Max-pooling & Pool size = 8, stride = 8 \\ Non-local neural network & F = 100 (1/2*Kernel size) \\ First Attention-Based BiLSTM & \\ Second Attention-Based BiLSTM & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The CNN (Convolutional Neural Networks)-attention-based BiLSTM (Bi-directional Long Short-Term Memory) with non-local neural network. \begin{table}
\begin{tabular}{l l} \hline \hline
**CNN** & \\ \hline Convolutional layer & Filter = 32, kernel size = 200, stride = 8 \\ Rectified Linear Unit & \\ Batch normalization + dropout (0.70) & \\ Max-pooling & Pool size = 8, stride = 8 \\ Non-local neural network & F = 100 (1/2*Kernel size) \\ First Attention-Based BiLSTM & \\ Second Attention-Based BiLSTM & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The CNN (Convolutional Neural Networks)-attention-based BiLSTM (Bi-directional Long Short-Term Memory). The reason why CNN-attention-based BiLSTM outperformed the other frameworks is that as the BiLSTM is performed after the CNN, the features of BiLSTM, as the further processing of CNN, can more accurately characterize the category of the sample. Thus focusing on the important feature of CNN-BiLSTM is more effective. We also noticed that the CNN-attention-based BiLSTM with a non-local neural network and the CNN-attention-based BiLSTM with CBAM, which focus on the CNN and BiLSTM simultaneously, performed worse than the CNN-attention-based BiLSTM did. We speculate that the reason for this different is that the attention mechanism performed on the CNN filters highlights the features that are important to the BiLSTM, which leads to the degradation of the overall model. As the CNN-BiLSTM with DA-NET and the CNN-BiLSTM with non-local neural network perform worse than the CNN-BiLSTM, DA-NET and the non-local neural network make the CNN-BiLSTM focus on certain invalid features. For specificity, the results showed that the CNN-attention-based BiLSTM also performed better than CNN-BiLSTM. The confusion matrixes of these frameworks are shown in Figure 11. It can be seen that CNN-attention based BiLSTM performed better for the detection of three levels compared with CNN-BiLSTM.