Tourism Ecological Efficiency Assessment Based on Multi-Source Data Fusion and Graph Neural Network

Current research on evaluating tourism’s ecological efficiency using multi-source data fusion and graph neural networks has notable limitations. At the data level, integrating diverse sources is difficult due to differences in format, quality, and meaning. Data cleaning and preprocessing can lead to information loss, and relying on a single source often fails to reflect the complexity of tourism ecosystems. At the model level, traditional methods struggle to identify unreliable data and lack scientific rigor in handling expected and unexpected outcomes. These issues reduce the accuracy and practical value of evaluation results. This paper introduces a new method for assessing tourism’s ecological efficiency based on multi-source data fusion and graph neural networks. First, we integrate tourism statistics, environmental monitoring, and socio-economic data into a comprehensive dataset. Then, we apply a graph neural network (GNN) model to uncover hidden relationships and patterns, enabling a more accurate assessment of tourism’s environmental impact. The method also analyzes how tourism’s ecological efficiency varies across time and regions. We validate the method through case studies of representative tourist destinations and discuss its application in tourism planning. Regression analysis based on a single data source yields a 2020 tourism ecological efficiency score of 72. In contrast, using multi-source data fusion and GNN, the score rises to 85—an improvement of 13 points. This study offers a new approach to evaluating tourism’s ecological efficiency, enhances our understanding of tourism ecosystems, and supports sustainable tourism development.