## 3 Materials and Methods
### Mechanism of Adaptive Recalibration with Excitation  
The SE mechanism plays a crucial role in dynamically recalibrating the importance of different feature channels, ensuring that the most influential factors in the tourist-flow prediction receive appropriate weights. The adaptive recalibration process begins with global feature aggregation through the Squeeze operation, which aggregates the global information from each subsystem to form a comprehensive descriptor representing the system state. This is achieved by applying global average pooling, which computes statistical representations of each feature channel, ensuring the model captures the overall feature importance rather than just localized information. In the context of tourism forecasting, each channel typically represents a region-level indicator, such as the average monthly temperature, tourism revenue, employment rate, or sentiment index. The global average pooling operation transforms such temporally fluctuating signals into stable scalar summaries by averaging across the spatial or temporal dimension. This process effectively filters out local noise or outliers and retains a high-level signal representing the long-term trend or structural state of each input variable. For instance, aggregating the temperature data yields a seasonal climatic context, while averaging the tourism revenue offers a macroeconomic baseline. These global descriptors serve as inputs to the Excitation network, enabling it to assign higher weights to the features most relevant to a given forecasting context.  
\[s_{c}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}x_{c}(i,j) \tag{6}\]  
where \(x_{c}(i,j)\) represents the value of the \(c\)-th feature channel at spatial position \((i,j)\), \(H\times W\) denotes the spatial dimensions of the feature map, and \(s_{c}\) is the aggregated global descriptor for the \(c\)-th channel.  
Next, a two-layer fully connected network is used to model the complex interactions between features. This involves a nonlinear feature transformation, which consists of dimensionality reduction, ReLU activation, and dimensionality expansion. Initially, the feature space is reduced using a fully connected layer:  
\[z=W_{1}s+b_{1} \tag{7}\]  
where \(W_{1}\) and \(b_{1}\) are learnable parameters. The nonlinearity is then introduced using a ReLU activation function:  
\[v=\text{ReLU}(z) \tag{8}\]  
Finally, the feature space is restored to its original dimension:  
\[u=W_{2}v+b_{2} \tag{9}\]After the transformation, a sigmoid activation function is applied to generate the excitation weights:  
\[w_{c}=\sigma(u_{c})=\frac{1}{1+e^{-u_{c}}} \tag{10}\]  
where \(w_{c}\) is the importance weight for the \(c\)-th feature channel. These excitation weights are then applied to the original feature channels, recalibrating them based on their importance:  
\[X_{c}^{\prime}=w_{c}\cdot X_{c} \tag{11}\]  
where \(X_{c}^{\prime}\) is the recalibrated feature, \(X_{c}\) is the original input feature, and \(w_{c}\) is the weight assigned by the excitation mechanism. This process enhances the influence of important features while suppressing less relevant ones, ensuring that the model focuses on the most critical indicators for sustainable tourism development.