###### Abstract
SE-TFF effectively combines local and global information, leveraging hierarchical feature structures and dynamic planning techniques to ensure accurate predictions, even with small datasets. * We incorporate a multi-task learning model into tourism forecasting, providing comprehensive predictions of key tourism indicators to support strategic planning and operational decisions. The model effectively analyzes various related factors, forecasting not only tourist volumes but also tourism-related economic indicators, such as income, lodging, and transportation consumption. Additionally, multi-task learning enables dynamic internal adaptation using continuity-based statistics, enhancing the model's generalization and adaptability. * The study validated SE-TFF's advantages under small-sample conditions regarding prediction accuracy, applicability, and real-world relevance. The experimental results confirm that despite the data scarcity and significant regional variations, SE-TFF effectively identified beneficial features, which enhanced the accuracy and robustness. The results further demonstrated broad applicability, providing accurate predictions in multiple regional contexts, including scenarios with limited samples. The remainder of the paper is structured to comprehensively address both practical and theoretical aspects of SE-TFF, ensuring clarity in motivation, methodology, empirical validation, and implications. Section 2 reviews state-of-the-art computational methods in tourism forecasting, with a focus on machine learning models and small-sample adaptation strategies. This sec tion highlights current challenges such as data sparsity, non-stationarity, and regional heterogeneity that motivate the need for a more robust forecasting framework. Section 3 introduces the proposed SE-TFF framework in detail, emphasizing how multiscale feature extraction and reinforcement-based dynamic weighting are integrated across environmental, economic, and social data streams. It elaborates on the model's internal architecture and design rationale, particularly its capacity to handle noisy, incomplete, or highly regionalized input data. Section 4 presents a series of comparative experiments designed to assess the predictive performance, component-wise contributions, parameter robustness, and computational efficiency of SE-TFF. Benchmarking against classical statistical, hybrid, and neural baselines demonstrates SE-TFF's advantage in accuracy, adaptability, and interpretability under sparse data conditions. Section 5 reports the empirical results in an objective manner, including performance metrics across matrix densities, SHAP-based feature attribution, Top-K sensitivity curves, and ablation study outcomes. Section 6 provides an in-depth discussion of the findings, contextualizing them within the existing literature, unpacking their methodological significance, and outlining practical implications for tourism management. It also identifies current limitations and suggests future research directions including multimodal data integration, lighter architectures, and enhanced temporal modeling. Section 7 concludes the paper by summarizing the key contributions of SE-TFF and outlining its potential as a scalable and interpretable decision-support tool for sustainable tourism forecasting. It also discusses the current limitations of the framework and proposes future research directions to address them.