Big Data-Based Study on High-Quality Ecotourism Development in the Middle and Lower Yellow River Basin

Sustainable ecotourism management in environmentally sensitive regions demands integrated information systems for heterogeneous environmental and socio-economic data processing. This study presents a big data-driven Environmental Decision Support System (EDSS) for sustainable ecotourism in the middle and lower Yellow River Basin. Built on Hadoop-Iceberg and H3 spatial indexing, the system integrates remote sensing, human mobility, social media sentiment, and statistical data. A spatio-temporal prediction framework combining spatial graph convolution, dilated temporal convolution, and cross-domain multi-head attention models ecological, economic, and social interactions. A reinforcement learning-based policy optimization module generates adaptive resource allocation strategies. Experiments show the framework yields a prediction Root Mean Square Error (RMSE) of 0.028 and increases the Eco-Economic Index by 6.9%. Results demonstrate big data systems' value in enhancing monitoring, management, and decision-making for sustainable watershed governance.