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

Currently, research on the evaluation of tourism ecological efficiency based on multi-source data fusion and graph neural networks has significant limitations: at the data level, the integration of multi-source data faces challenges related to format, quality, and semantic differences; the complexity of cleaning and preprocessing may lead to information loss, and the limitations of a single data source are prominent, making it difficult to comprehensively cover the complex features of the tourism ecosystem; at the model level, traditional evaluation models cannot accurately identify ineffective sources, the handling of expected vs. unexpected outputs is not sufficiently scientific, and adapting to the dynamic demands of tourism ecological efficiency evaluation is challenging, which restricts the accuracy and application value of the evaluation results. This paper proposes a tourism eco-efficiency assessment method based on multi-source data fusion and graph neural networks. First, a comprehensive dataset is constructed by integrating multi-source information such as tourism statistics, environmental monitoring data, and socio-economic data. Then, the GNN model is used to mine the intrinsic connections and patterns in the data to more accurately evaluate the impact of tourism activities on the ecological environment. In addition, the distribution characteristics of tourism eco-efficiency in different periods and geographical regions were analyzed by considering spatial and temporal factors. Through case studies of typical tourism destinations, the effectiveness of the proposed method is verified, and its application value in practical tourism management and planning is discussed. Regression analysis was used to estimate tourism eco-efficiency based on a single data source. The data from 2015 to 2020 are selected to calculate the correlation coefficient between the growth rate of tourism revenue and environmental quality indicators. Based on the regression analysis of a single data source, the resulting tourism eco-efficiency score was 72 points in 2020. Using multi-source data fusion and graph neural network, the tourism eco-efficiency score was 85 points in the same year, 13 points higher than the traditional method. This study not only provides a new method for tourism eco-efficiency assessment but also helps to deepen our understanding of the complexity of tourism ecosystems and provides scientific support for the sustainable development of tourism destinations.