Urban expansion and construction significantly affect natural hydrological cycles, with extreme climate conditions leading to an increasing frequency of waterlogging phenomena in built environments year by year. Analyzing the internal coupling relationship between urban construction and waterlogging risk while revealing the root causes of waterlogging in urban built environments holds important research value and represents a crucial step in filling theoretical gaps in urban flood prevention planning supported by multiple disciplines. This study systematically investigates the influence mechanism of urban surface basement environment on waterlogging risk, taking the built-up area of Shenyang City as an example. Through comprehensive statistical analysis of surface basement environmental factors including green coverage rate, water body proportion, impervious surface proportion, forest type distribution, elevation difference distribution, exotic soil distribution, underground construction distribution, and drainage facility standards, this research employs multifactor linear regression analysis methods to quantify the weight coefficients and correlations of each factor. The methodology encompasses digital elevation model (DEM) construction for hydrological and hydrodynamic modeling, utilizing equal volume method for inundation analysis and calculating submersion depths of land patches within catchment areas. Geographic Information System (ArcGIS) spatial analysis platform modules were employed for reclassification tools to divide factors according to risk levels, adopting 500-meter grid statistics and natural breaks method to classify waterlogging intensity into four levels corresponding to 50, 20, 10, and 5-year return period rainfall scenarios. The research framework incorporates SPSS statistical analysis including likelihood ratio Omnibus testing, correlation analysis, standardized ridge regression trace analysis, Vector Autoregression (VAR) model variance decomposition, and multidimensional scaling analysis distance matrix to comprehensively evaluate the dynamic contributions of different basement environment factors to waterlogging risk prediction error variance. Results demonstrate that drainage facility standards constitute the most significant influencing factor on waterlogging risk intensity, with a standardized coefficient of 0.260 and significance level of p=0.000, indicating that higher drainage facility standards correspond to lower waterlogging risk intensity. The likelihood ratio chi-square value of 26.633 with eight degrees of freedom and p=0.001 confirms the overall model significance, reflecting strong explanatory capability of basement environment factors as a collective influence on waterlogging risk. Hardening proportion and underground construction distribution exhibit relatively significant positive impacts on waterlogging risk intensity, while other factors including green coverage rate, water body proportion, forest type distribution, elevation difference distribution, and exotic soil distribution show nonsignificant correlations due to excessive interference factors. VAR model variance decomposition reveals that with increasing prediction periods, artificial environment factors demonstrate significantly increased contributions, reflecting long-term cumulative effects, with drainage facility standardscontributing 6.568% and hardening proportion contributing 5.866% as dominant factors, while underground construction distribution contributes 2.531% as a secondary factor. Natural regulation factors such as green coverage rate and water body proportion consistently maintain contributions below 1.9%, reflecting insufficient utilization of urban green spaces and water bodies’ storage and regulation functions, possibly due to inadequate scale, scattered distribution, and degraded ecological functions of urban green spaces in the old urban core areas within the first and second ring roads of Shenyang’s built-up area. The correlation analysis reveals that although green coverage rate and water body proportion theoretically provide natural waterlogging mitigation capabilities, their actual effectiveness remains limited in high-intensity urban development areas, suggesting threshold effects where natural factors only demonstrate effective flood prevention when reaching certain scales. Distance matrix analysis indicates relatively small distances between green coverage rate and water body proportion (8.79), and between hardening proportion and green coverage rate (8.109), suggesting synergistic effects in spatial distribution or functionality, while forest type distribution maintains large distances from other factors (43.392 with green coverage rate), indicating independent influence mechanisms. The study fills theoretical gaps in urban flood prevention planning under multidisciplinary support, providing scientific evidence for urban waterlogging risk management and demonstrating that while drainage facility standards currently dominate waterlogging risk management, flood prevention strategies relying solely on gray infrastructure present limitations. The multi-factor synergistic mechanism of urban basement environment reveals that future urban flood prevention should adopt comprehensive “gray-green integration” strategies, optimizing drainage systems while fully utilizing natural patches’ storage and regulation potential. This research establishes a “data-model-mechanism” three-element driving framework that can guide future urban built environment waterlogging risk studies through integration of multi-source data and interdisciplinary methods, enhancing the robustness of risk prediction and decision support value for urban surface environment renewal and resilience enhancement strategies.