The multifunctionality of urban parks significantly enhances citizen well-being. Uncovering the potential spatiotemporal non-stationary relationships between the built environment and park vibrancy will provide more effective information for urban green space governance. This study estimates park vibrancy based on Baidu heatmap data and depicts the spatiotemporal distribution of vibrancy across three types of urban parks in Chengdu. A Geographically and Temporally Weighted Regression (GTWR) model was applied to explore the dynamic spatiotemporal impacts of internal and external built-environment variables on park vibrancy. The results indicated that the GTWR model demonstrated superior goodness-of-fit compared to the linear regression model. The associations between multidimensional built-environment factors and park vibrancy exhibited notable spatiotemporal heterogeneity. Furthermore, the influence of greenery on park vibrancy was highly context-dependent, varying across perspectives (2D vs. 3D) and granularities (overall vs. local). For instance, normalized difference vegetation index (NDVI), reflecting overall green volume, was negatively associated with park vibrancy, whereas the coverage and fractal dimension of tree canopy, characterizing local vegetation structure, showed predominantly positive associations. These findings provide rich analytical perspectives and effective methodologies for examining and revealing the mechanisms influencing urban-park vibrancy, and can guide policymakers and urban planners in formulating more targeted park-management strategies.