Multi-Model Fusion for Evaluating the Impact of Garden Design on Urban Mental Health

With the acceleration of urbanization, the mental health problems of urban residents are becoming increasingly serious. Environmental design, especially garden design, is considered an important factor in alleviating this problem. However, existing research has mostly focused on qualitative analysis or quantitative evaluation of a single model, lacking comprehensive, multi-dimensional model analysis. To make up for this deficiency, this paper proposes an overall model combining multiple regression model, support vector machine (SVM) and K-means cluster analysis to evaluate the comprehensive impact of garden design on mental health. Through multi-model fusion, the complex nonlinear relationship between garden design characteristics and mental health can be effectively handled, and more accurate prediction and analysis results can be provided. Experimental results show that the overall model performs well in regression analysis, classification tasks and cluster analysis, and has significant advantages over the traditional single model. In addition, ablation experiments verify the importance of each component in the model and show that the combination of multivariate regression, SVM and K-means can improve the overall performance and stability of the model. This study provides a new analytical method for the relationship between garden design and mental health and provides theoretical support for mental health intervention strategies.