## 7 Conclusions and Limitations  
This paper proposes SE-TFF, a novel framework for forecasting cross-regional tourist flow based on dynamic multi-scale feature extraction from environmental, economic, and social subsystems using the Squeeze-and-Excitation (SE) mechanism. Unlike traditional forecasting models such as SVR, ARIMA, ANN, hybrid recommenders, and ARIMAX, SE-TFF adaptively recalibrates feature relevance using a channel-wise SE module, and dynamically balances multi-task objectives via reinforcement learning (RL). These innovations enable SE-TFF to deliver context-aware, region-sensitive predictions under sparse and heterogeneous data conditions. Extensive comparative experiments demonstrated that SE-TFF significantly outperformed all baseline methods. At a matrix density of 20%, SE-TFF reduced MAE by 56.5% and RMSE by 65.6% compared to the best-performing baseline (ARIMAX). These gains highlight SE-TFF's robustness under limited data availability and its ability to generalize across diverse scenarios. The SHAP-based interpretability analysis revealed that environmental and economic features--especially climate variation, tourism revenue, and employment opportunities--were the most influential predictors of tourism flow. In contrast, social features, such as resident satisfaction and tourist experience, showed comparatively lower impacts, particularly in post-pandemic settings, suggesting opportunities for refining social-data acquisition and integration. Parameter sensitivity experiments further emphasized the importance of the Top-K feature set. Selecting the top 25-30 features consistently balanced precision and generalizability, especially in sparse or noisy input regimes. Ablation studies confirmed that both the SE and RL modules are indispensable; removing either resulted in substantial degradation in accuracy and adaptability. Together, these components offer synergistic advantages in learning from real-time signals and optimizing across multiple competing objectives. From a practical perspective, SE-TFF offers several actionable insights for tourism planners and policymakers. Its ability to dynamically prioritize region-specific indicators supports targeted interventions, such as adjusting infrastructure or marketing efforts in response to economic or environmental stress signals. The model's multi-task outputs--including forecasts of tourist arrivals, revenue, and accommodation demand--enable comprehensive decision support for planning, investment analysis, and emergency response. Moreover, SHAP interpretability facilitates transparent communication of policy rationale to stakeholders. Despite its demonstrated advantages, SE-TFF has several limitations:  
First, the model's effectiveness depends on the quality and completeness of input data. In extremely sparse or imbalanced scenarios--especially in regions with under-reported tourism statistics--predictive accuracy may decline. Future research could incorporate data enhancement techniques such as matrix completion, generative imputation (e.g., VAEs), or graph-based reconstruction to improve data coverage. Second, SE-TFF's architecture introduces computational overhead. The SE and RL modules, while effective, may pose deployment challenges in real-time systems. Lightweight approximations (e.g., Linformer, Performer) or off-policy RL algorithms may improve inference speed and stability. Third, current input channels rely primarily on structured data. The exclusion of real-time social signals--such as online sentiment, geo-tagged travel flows, or mobile activity--limits responsiveness to short-term disruptions. Future extensions may integrate multimodal or streaming inputs using cross-attention or co-training mechanisms. Fourth, the model lacks explicit temporal decomposition for cyclical tourism patterns (e.g., holidays, festivals, school calendars). Integrating STL-based trend separation, periodic embeddings, or temporal self-attention layers could enhance performance on seasonally-driven demand shifts.