###### Abstract  
Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016-2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with \(0.92\times 10^{3}\) average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF's ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. 2025 14 July 2025 22 July 2025 22 July  
to capture high-order cross-factor interactions; none of these three cope well with missing or highly noisy inputs [7]. Consequently, these methods provide limited guidance when the transportation capacity, weather shocks, economic fluctuations, and social sentiment jointly drive demand. Traditional models also lack mechanisms to exploit hierarchical or irregular sampling, leading to degraded accuracy in real-world, noisy contexts [8]. Despite recent progress, no existing approach integrates multi-scale spatiotemporal features, performs adaptive channel re-weighting, and supports multi-task prediction under extreme data sparsity. Current studies typically tackle at most one or two of these aspects in isolation, leaving a comprehensive solution absent from the tourism-forecasting literature. To address this gap, we propose SE-TFF, a tourism-flow forecasting method based on multi-scale representations extracted at multiple temporal or spatial resolutions from environmental, economic, and social subsystems using SE-Net. This method integrates regional characteristics into a unified forecasting framework that effectively captures these complex feedback mechanisms. To enhance the forecasting accuracy, the proposed SE-TFF incorporates the Squeeze-and-Excitation (SE) mechanism. This mechanism automatically identifies and emphasizes critical features [9] with significant impacts on tourism demand [10]. By employing trend analysis and extensive data input, the SE mechanism selectively enhances features based on situational relevance, thus improving the model's ability to abstract essential regional and environmental characteristics and increasing the forecasting accuracy and applicability. Central to our method is SE-Net, which dynamically assigns importance weights across multiple [11] input channels--environmental, demographic, and economic features--drawing inspiration from [PERSON]'s multi-scale modeling approach [12]. SE-Net's parallel processing and integration of multi-dimensional features significantly enhance the predictive capabilities across diverse [13] regions, emphasizing critical features to boost the error tolerance [14] and accuracy. Specifically, the primary contributions of this work includes the following:  
* We propose a tourism forecasting framework (SE-TFF) specifically designed for scenarios characterized by strong regional specificity, limited data availability, and challenging environmental conditions.