Development strategy of rural low-carbon ecotourism under the background of big data and the Internet of Things

Abstract Through the use of multimodal data, including behavioral, physiological, and textual sentiment inputs, this study presents a deep learning-based framework for sustainable rural ecotourism that monitors the mental health of employees working in rural ecotourism enterprises. The system exhibits excellent predictive performance (ROC-AUC = 0.945, F1-score = 0.913, and accuracy = 89.3%, equivalent to 0.893). It provides early warnings for stress, anxiety, and burnout up to 5.6 days earlier than existing approaches by utilizing a complex network with attention processes. In this case, quantifiable gains in infrastructure, employment, and income in rural tourism areas are considered indicators of country-level growth. With a flexibility score of 0.97, the recommendation process exhibits outstanding stakeholder collaboration and flexibility. The framework promotes the overall objective of sustainable economic development in rural areas by facilitating flexible site construction, efficient resource management, and real-time monitoring.