An in-depth exploration of the coupling and coordination relationship between urban social vulnerability and human activity intensity can provide a scientific basis for urban sustainable development, planning optimization, and policy decisions. Based on multi-source remote sensing data and social-economic statistics in 2021, Zhengzhou’s social vulnerability and human activity intensity were quantified. Subsequently, a coupling coordination degree model was applied to reveal the extent of coordination between these two factors. Finally, the geographical detector was used to analyze the impact of driving factors on the degree of coupling coordination. Results indicated that the spatial distribution pattern of social vulnerability and human activity intensity in Zhengzhou exhibits a gradual attenuation trend from the center toward the periphery, indicating a strong correlation between the two factors. The regions with high coupling coordination degrees were primarily concentrated in the central region. The total output value of the primary industry, patch aggregation index, per capita cultivated land area, and human activity intensity were identified as key drivers affecting changes in coupling coordination degree. These factors exhibit evident synergistic enhancement effects, resulting in comprehensive impacts on the spatial distribution of coupling. This study can provide a reference for urban development decision-making.

Systematically assessing the supply–demand disparities of urban–rural ecosystem services (ES) is a key pathway to optimizing resource allocation, promoting urban–rural integration and advancing regional sustainable development. Taking Zhengzhou City as a case study, this research evaluates and compares urban–rural differences across four dimensions: potential supply, actual supply, real human needs (RHN), and effective supply. Furthermore, focusing on actual supply, the study integrates a geographical detector and Bayesian belief network to identify key driving factors, delineate optimal optimization zones, and propose differentiated management strategies. The results show that: (1) Urban RHN accounts for 69.70% of the total in Zhengzhou, with a spatial pattern of “higher in the east and core, lower in the west and periphery”, and the internal heterogeneity is significantly greater than that of rural areas. (2) Potential supply is “higher in rural areas and in the west”, whereas actual supply is concentrated in central urban districts, reflecting a net service flow from rural to urban areas. (3) High-level effective supply areas cover 37.28% of urban regions, about 18 percentage points higher than rural regions. Rural deficits are primarily caused by low conversion efficiency of supply rather than insufficient potential. (4) Optimal urban optimization zones are mainly distributed in peripheral urban streets, while rural zones are concentrated in eastern townships. Through multidimensional supply–demand comparison and spatial optimization, this study provides a scientific basis for the coordinated enhancement of urban–rural ES, differentiated governance and regional sustainable development.