Public transportation serving urban tourism has a positive impact on sustainable urban development. With the rapid rise of “subway tourism” in China and the emergence of numerous popular attractions, the pedestrian spaces connecting subway stations and attractions are important public spaces for tourists’ perception. Identifying, cognizing, and perceiving the pedestrian spaces around subway stations near popular tourist attractions from the tourists’ perspective holds significant value for promoting station–city coordination and enhancing spatial quality. This paper establishes an optimization design framework for the pedestrian spaces around subway stations near urban popular tourist attractions, moving from identification to cognition and perception. Taking Tianjin, China as an example, we collected 11,405 travelogue data entries and street network data of the subway stations around popular attractions. (1) We constructed an LDA topic model to identify popular tourist attractions; (2) we applied space syntax to understand the features and forms of the pedestrian spaces around subway stations; (3) we utilized the ROST-CM network text analysis method to analyze tourists’ overall perception of the pedestrian spaces around subway stations; (4) we proposed suggestions for optimization. The contribution of this study lies in constructing a vertical analytical framework that transitions from “identification” to “cognition” to “perception”. The cognitive and perceptual dimensions can mutually corroborate some of their findings but are not interchangeable. Future urban spatial optimization research should consider both cognitive and perceptual dimensions, enhancing the comprehensiveness of the human-centered perspective. The research results provide empirical references and guidance for the management and practice of urban space renewal around subway stations near major cities’ tourist attractions.

Linear green waterfront corridors are important public open spaces where movement, spatial experience, and recreational behavior unfold continuously along paths. However, the visual–spatial conditions associated with visitor behavior in such settings remain insufficiently understood, particularly from the perspective of sequential spatial experience. Using the Tongzhou section of the Grand Canal in Beijing as a case study, this study examined how visitor movement and staying rhythms vary across visual–spatial environments along a linear corridor. Sixteen visual–spatial indicators were derived from 1505 georeferenced panoramic images, and approximately 1.65 million GPS trajectory points were used as external behavioral evidence. Five latent visual–spatial states were identified along the corridor sequence, and the contributions of different indicators to state differentiation were further interpreted. The results show that the corridor was organized into differentiated visual–spatial states with clear continuity and gradual transitions, and that visitor behavior differed significantly across these states. High visit intensity did not necessarily correspond to a high stay ratio, and lower movement speed did not uniformly indicate stopping. The strongest state-differentiation signals were associated with depth-informed structural variables, especially the closely related foreground proportion and average depth, together with waterfront-interface conditions. These findings extend current understanding of visual–spatial environment–behavior relationships in linear public green spaces and provide methodological support for the planning, design, and management of waterfront and other linear public green environments.