Basketball is a popular sport worldwide, and many researchers have utilized various machine learning models to predict the outcome of basketball games. However, prior research has primarily focused on traditional machine learning models. Furthermore, models that rely on vector inputs tend to ignore the intricate interactions between teams and the spatial structure of the league. Therefore, this study aimed to apply graph neural networks to basketball game outcome prediction, by transforming structured data into unstructured graphs, to represent the interactions between teams in the 2012–2018 NBA season dataset. Initially, the study used a homogeneous network and undirected graph to build a team representation graph. The constructed graph was fed into a graph convolutional network, which yielded an average success rate of 66.90% in predicting the outcome of games. To improve the prediction success rate, feature extraction based on the random forest algorithm was combined with the model. The fused model yielded the best results, and the prediction accuracy was improved to 71.54%. Additionally, the study compared the results of the developed model with previous studies and the baseline model. Our proposed method considers the spatial structure of teams and the interaction between teams, resulting in superior performance in basketball game outcome prediction. The results of this study provide valuable insights for basketball performance prediction research.

Environmental pollution has become a significant issue, and environmental protection within the realm of sports has garnered increasing attention from various sectors of the academic community. This study takes the public’s intention to purchase sports stadiums as the research object, introduces two mediating variables of green value perception and sense of accomplishment, builds a hypothetical model from the perspective of motivation protection theory, and explores the influence of perceived functional risk of green sports stadiums on consumption intention through SEM analysis based on the samples of the recovered questionnaires (N = 1073). The results are as follows: first, there is a negative influence between perceived functional risk and green sports stadium consumption intention, and the lower the perceived functional risk of green sports stadiums, the more consumers can promote their choice of green sports stadiums when they consume in stadiums; second, green value perception plays a mediating role in the relationship between perceived functional risk and green sports stadium services consumption intention; third, a sense of accomplishment plays a mediating role in the relationship between perceived functional risk and willingness to consume green sports stadium; fourth, green value perception and a sense of accomplishment play a chain mediating role between perceived functional risk and willingness to consume in the context of green sports stadiums. This study aims to contribute to the popularization and development of green sports stadiums in China and to provide green sports stadium builders and managers with more targeted and effective marketing strategies.