With the deepening implementation of transit-oriented regional regeneration, assessing the spatial adaptability of metro station areas has become a critical issue in the governance of high-density built-up urban districts. Traditional regional analysis methods often overlook the actual configuration of street networks and the quality of micro-scale environments, making it difficult to identify public space deficiencies within metro catchment areas in densely populated urban settings. This study focuses on typical residential-oriented metro stations in Shanghai’s central city and proposes a dual-dimensional spatial simulation model that integrates accessibility and streetscape experience. By combining pedestrian network analysis, semantic segmentation of street view imagery, and principal component–based clustering, a micro-scale diagnostic framework is established. The model identifies differentiated spatial coupling types and supports the formulation of targeted renewal strategies. The results confirm the model’s effectiveness and applicability in supporting stock-based regional regeneration, while also highlighting both its methodological novelty and its practical applicability for regeneration planning.