Exposure to high-quality visual environments characterized by features such as structural order, biophilic/natural elements, and positive atmosphere is important for walking experience within community life circles (CLCs). However, compared with the static configuration of visual environments within CLCs, dynamic walking-based exposure may highlight unpredictable encounter areas and heterogeneous environmental quality. Neglecting this mismatch may misdirect environmental interventions and limit their health-promoting potential. This study aims to integrate multidimensional visual environment features into interpretable scene clusters to improve comparability and measure configuration–exposure mismatches across CLCs at scale. We examine 1262 CLCs in Harbin, China, identifying visual scene clusters from 67,840 street-view images and extracting exposure frequencies from 981,500 mobility tracks. The results show that (1) eight scene clusters effectively describe the complex visual environments of CLCs; (2) significant small-to-moderate mismatches exist between configuration and exposure; (3) the trend of commute-related walking activity is often consistent with strengthened exposure to high-disorder scenes and weakened exposure to some high-quality scenes with positive atmospheres. This study provides a data-driven framework for identifying mismatches in both the intensity and spatial distribution of visual scene configuration and exposure, supporting refined community environmental governance.