The impact of the built environment (BE) on urban vitality (UV) has become a key issue in the field of urban planning. However, few studies have explored the impact of the BE on UV from the perspective of urban function zones (UFZs). Taking the central urban area of Tianjin as an example, this paper explores the nonlinear influences and threshold effects of the BE on UV using machine learning methods. It also reveals the spatiotemporal variations in UV across different UFZs during the daytime and nighttime on weekdays and weekends. The results show the following: (1) Education and culture zones showed the highest UV during weekday daytime, while commercial zones dominated at other times. Industrial zones remained the least active throughout. Residential zones demonstrated higher nighttime UV than daytime UV on weekdays, with the opposite pattern observed on weekends. Public service zones maintained a comparable level of UV between the daytime and nighttime on weekdays. Other function zones generally displayed higher daytime UV. During the daytime on weekends, all function zones except industrial zones demonstrated higher UV compared to other time periods. (2) In commercial zones, the floor area ratio (FAR) exerted the strongest influence, displaying distinct threshold effects. Residential zones showed dual sensitivity to building height (BH) and the FAR. Public service zones were predominantly influenced by Road Density (RD) and Bus Station Density (BSD). RD exhibited higher marginal utility for enhancing UV during the nighttime. Education and culture zones were significantly influenced by the FAR, RD, and POI Density (POID).

The land surface temperature (LST) in the central urban area has shown a consistent upward trend over the years, exacerbating the surface urban heat island (SUHI) effect. Therefore, this study focuses on the central urban area of Chengdu, using blocks as the research scale. The Gradient Boosting Decision Tree (GBDT) model and SHAP values are employed to explore the nonlinear effects of human settlements (HS) on LST across different seasons. The results show that (1) At the block scale, the overall impact of HS on LST across all four seasons tracks the following order: built environment (BE) > landscape pattern (LP) > socio-economic development (SED). (2) LP is the most important factor affecting LST in summer, while the BE has the greatest influence on LST during spring, autumn, and winter. (3) Most HS indicators exhibit seasonal variations in their impact on LST. The impervious surface area (ISA) exhibits a significant positive impact on LST during spring, summer, and autumn. In contrast, the nighttime light index (NTL) and functional mix degree (FMD) exert a significant negative influence on LST in spring, autumn, and winter. Additionally, the normalized difference vegetation index (NDVI) negatively affects LST in both spring and summer. Moreover, connectivity (CNT) and functional density (FPD) demonstrate notable threshold effects in their influence on LST. (4) Certain HS indicators exhibit interaction effects, and some combinations of these indicators can effectively reduce LST. This study reveals HS–LST interactions through multidimensional analysis, offering block-scale seasonal planning strategies for sustainable urban thermal optimization.