This study investigated the relationship between urban form and land surface temperature (LST) using the Multi-access Geographically Weighted Regression (MGWR) model. A case study on Nanjing City was conducted using building data, point-of-interest (POI) data, land use data, remote sensing data, and elevation data. The results show that the MGWR model can reveal the influence of altitude, urban green space, road, building height (BH), building density (BD) and POI on LST, with a superior fitting effect over the geographically weighted regression model. LST in Nanjing exhibits a significant spatial differentiation, and the distribution of LST hotspots is spatially consistent with the level of urban construction. In terms of the two-dimensional landscape pattern, LST decreases with altitude and increases with POI. In terms of the three-dimensional structure, building height has a positive correlation with LST. POI, urban roads, and urban buildings positively affect LST, while urban green space and altitude negatively affect LST. The results of this study were verified against existing findings. The LST of areas with high-rise and super high-rise buildings is lower than that of areas with mid-rise building, which can be attributed to the large number of shadow areas formed by high-rise and super high-rise buildings. A similar phenomenon was also observed between areas with medium- and high-density buildings. These findings provide a reference for urban architecture planning and can help to develop urban heat island adaptation strategies based on local conditions.