## 3 Proposed Method
### Hierarchical Deeper Attention Module  
The semantic association information extracted at this time is often insufficient to represent the multi-scale meaning of the text at this stage due to the different number of attention processing layers of these implicit semantic vectors mined by backbone attention computing. The hierarchical deeper attention module is used to utilize lower-level semantic representations and further mine the associated information in the semantics.  
Assuming the presence of M stages in the backbone network architecture, and that each stage will output an implicit semantic representation vector \(q_{i}^{l},l\in\text{0,1,2,}\cdots,M,\)\(i\in\text{0,1,2,}\cdots,N,\) represents the implicit semantic representation vector obtained after the \(i\)th paragraph of text passes through the \(l\)th stage. This semantic vector needs to be further processed. The process is divided into \(n\) same stages to mine semantic information. The detailed calculation steps of each stage are as follows:  
\[\left\{\begin{array}{l}Q_{\text{act}}^{0}=q_{i}^{l},\\ Q^{l}=Attention(Q_{\text{act}}^{0}),t\in\text{0,1,2,}\cdots,n,\\ Attention(q_{i}^{l})=f\left(Q=q_{i}^{l},K=q_{i}^{l},V=q_{i}^{l}\right),\\ f(Q,K,V)=Softmax\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V,\end{array}\right. \tag{5}\]where \(Q^{t}\in R^{Seq^{\prime}\times E}\) represents the semantic vector obtained by the attention calculation processing of the \(t\)th stage, and \(Q^{t}_{\text{act}}\) represents the final output after activating the \(t\)th stage. \(Q^{0}_{\text{act}}\) is initialized as the implicit semantic vector hierarchically outputted by the backbone network. The \(Attention()\) function is the same as the attention mechanism in the Transformer. The multi-head attention calculation implemented in this network architecture is a parallel computing architecture and a non-serial computing mode, ensuring the improvement in model calculation efficiency. The normalized exponential function \(softmax()\) is an activation function:  
\[Softmax(z_{i})=\frac{e^{z_{i}}}{\sum_{c=1}^{C}e^{z_{c}}}, \tag{6}\]  
where \(z_{i}\) is the result of the \(i\)th node's calculation, while \(C\) denotes the number of output nodes, namely, the quantity of categories for classification.  
After the attention calculation is completed, activation processing is performed. This process can be expressed as follows:  
\[Q^{t}_{\text{act}}=\tanh(Q^{t}), \tag{7}\]  
where \(\tanh\) represents the hyperbolic tangent function:  
\[\tanh(z)=\frac{e^{z}-e^{-z}}{e^{z}+\ e^{-z}}. \tag{8}\]  
The model will retain the initial mapped text vector \(p_{0}\in R^{Seq^{\prime}\times E}\) as an input into the deep attention calculation. In the last stage, the model will no longer perform activation processing and only perform attention calculations. After passing through the hierarchical deeper attention module, the text representation vector \(Q^{n}\in R^{Seq^{\prime}\times E}\) will be obtained. We can obtain multi-scale and multi-dimensional fine-grained semantic information by further mining the hierarchical implicit semantic information in the backbone network, which facilitates subsequent models to understand text semantics.