## 3 Proposed Method
### Deep Learning Framework
#### 3.3.2 LSTM Timing Sequence Processing Module  
LSTM is a variant of the recurrent neural network (RNN). When the input and output of the training sample are uncertain, that is, when the sample dimensions are not unique, DNNs (deep neural networks) and CNNs often cannot achieve ideal results, and RNNs are a time series-based neural network model used to train data with continuous and varying lengths. Samples such as continuous speech or continuous text based on time series can achieve better results. However, RNNs can only handle short-term dependencies and cannot handle long-term dependencies. For example, when sentences are long, gradients accumulated at the end of the sequence tend to disappear or explode. Therefore, LSTM came into being, which makes many changes to RNN while maintaining the overall recurrent structure. Three gates are added to control the behavior of the recurrent unit, namely the input gate, forget gate, and output gate [31]. The input gate is used to control the receiving input of information; the forgetting gate is used to control the state of the cyclic unit before forgetting; the output gate is used to control the generation of output. Use Sigmoid to normalize the input to the interval [0, 1], where 0 represents completely giving up information and 1 represents completely receiving information. The LSTM structure is shown in Figure 8.  
Where \(h_{t}\) is the cell state at time t and \(x_{t}\) is the network input at time t. Specifically, a timestep from LSTM is calculated as follows:  
\[\begin{array}{l}f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)}\\ i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ \tilde{c_{t}}=tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c})\\ c_{t}=f_{t}*c_{t-1}+i_{t}*\tilde{c_{t}}\\ o_{t}=\sigma(W_{0}\cdot[h_{t-1},x_{t}]+b_{0})\\ h_{t}=o_{t}*\tanh(c_{t})\end{array}\]  
where \(f_{t}\) is the value of forgetting gate; \(i_{t}\) is the value of the input gate; \(\tilde{c_{t}}\) is the cell state candidate; \(c_{t}\) is the cell state; \(o_{t}\) is the value of output gate; \(h_{t}\) is the value of hidden layer state, and \(W\) and \(b\) are weights and biases. For LSTM, as long as the form of data input is designed, several layers of neural network cells can be directly stacked. In this paper, the features are extracted through a GCN, and then the dimension of output features can be changed by using linear function. The complete structure of LSTM is shown in Table 2. After the input layer, three layers of the long- and short-term memory cell with a feature number of 512 are stacked, and then the feature output at the last moment is extracted. The  
\begin{table}
\begin{tabular}{c c c} \hline
**Layer** & **Shape** & **Parameter** \\ \hline Graph convolution & \(128\times 128\) & Number of Features = 128 \\ Graph convolution & \(64\times 64\) & Number of Features = 64 \\ Full connection & 512 & Number of Features = 512 \\ \hline \end{tabular}
\end{table}
Table 1: Graph convolutional neural network parameters.  
Figure 8: LSTM recurrent cell structures.  
predictive probability distribution of the model is obtained through a full connection layer and the output layer.  
Softmax is expressed as follows:  
\[\hat{y}=argmax(\frac{\hat{y}_{i}^{\beta i}}{\sum_{i=1}^{N}{w_{i}^{2}}})\]  
in which \(\hat{y}\) is the output category, that is n. The loss function uses the common L2 regularized cross entropy:  
\[loss=-\sum_{i}^{n}{y_{i}log(\hat{y_{i}})}+\lambda\left\|\sum_{j=1}^{m}{w_{j}^ {2}}+b_{j}^{2}\right\|\]  
where \(y_{i}\) is the label book; n is the output class; and m is the number of samples. Here, we dichotomize to take n as 2.