Urban perception research has long focused on global metropolises, but has overlooked many cities with complex functions and spatial structures, resulting in insufficient universality of existing theories when facing diverse urban contexts. This study constructed an analytical framework that integrates street scene images and interpretable machine learning. Taking Zhengzhou City as the research object, it extracted street visual elements based on deep learning technology and systematically analyzed the formation mechanism of multi-dimensional urban perception by combining the LightGBM model and SHAP method. The main findings of the research are as follows: (1) The urban perception of Zhengzhou City shows a significant east–west difference with Zhongzhou Avenue as the boundary. Positive perceptions such as safety and vitality are concentrated in the central business district and historical districts, while negative perceptions are more common in the urban fringe areas with chaotic built environments and single functions. (2) The visibility of greenery, the openness of the sky and the continuity of the building interface are identified as key visual elements affecting perception, and their directions and intensifies of action show significant differences due to different perception dimensions. (3) The influence of visual elements on perception has a complex mechanism of action. For instance, the promoting effect of greenery visibility on beauty perception tends to level off after reaching a certain threshold. The research results of this study can provide quantitative basis and strategic reference for the improvement in urban space quality and humanized street design.

Landscape Visual Assessment (LVA) has long examined how landscape elements influence visual perception and aesthetic response, yet the question of which elements attract attention remains underexplored in historic parks. Compared with urban parks, streetscapes, or natural landscapes, historic parks are shaped by a distinctive combination of natural features, cultural structures, and historically embedded symbolic meanings. In this study, we categorize landscape elements in historic parks into Natural Landscape Elements (NLE) and Cultural Landscape Elements (CLE), and investigate their relative visual salience in three historic parks in Beijing: Taoranting Park, the Summer Palace, and Beihai Park. We adopt a multi-method exploratory framework integrating cognitive maps, eye-tracking experiments, computational vision analysis, and GIS/UGC-based spatial analysis. The study draws on 30 cognitive maps, participant-level eye-tracking data from 30 valid participants, supplementary heatmap-based computational image analysis, and large-scale geotagged photo-density data. The results show that, within the exploratory sample, CLE were more frequently associated with strong memory impressions than NLE. In the pooled eye-tracking analysis, CLE showed higher attention scores overall, but this pattern was not stable across all parks and was strongly context-dependent. The computational and spatial analyses further suggest that attention distribution is influenced not only by the presence of specific elements, but also by color contrast, the spatial coupling of CLE and NLE, and the broader organization of park scenes and visitor activity zones. Rather than proposing a universal model of visual attention in historic parks, this study offers an exploratory, context-sensitive account of how cultural and natural landscape elements jointly shape attention across perceptual and spatial scales. The findings contribute to landscape visual assessment research by extending it into the specific setting of historic parks and by demonstrating the value of combining perceptual, computational, and spatial methods in a complementary framework.

Rapid modernization and commercialization have weakened the cultural symbolism and social functions of many urban public spaces, yet the mechanisms through which public art contributes to the formation of the spirit of place remain insufficiently theorized and empirically tested. Drawing on Bourdieu’s field theory, this study conceptualizes public art as a cultural-capital intervention situated within a structured field of relations among artists, public authorities, and everyday users, and develops a mediation model linking the physical interactive attributes of public art, perceived spatial interaction, and the perceived formation of the spirit of place. An on-site questionnaire survey at 18 public-art sites in Ningbo, China, yielded 323 valid responses. Pearson correlation analysis, structural equation modeling (SEM), and bootstrap mediation tests were employed. The results show that physical interactive attributes significantly and positively affect perceived spatial interaction, which in turn significantly enhances the perceived formation of the spirit of place (p < 0.01). Perceived spatial interaction partially mediates this relationship (indirect effect = 0.183), accounting for 44.3% of the total effect; thus, all three hypotheses are supported. Emotional and cognitive interaction are particularly salient, whereas opportunities for embodied psychological interaction and immersive experience remain comparatively limited. Read through the lens of field theory, these findings indicate that public art converts cultural capital into place meanings only when its physical configuration and spatial setting afford sustained interaction, thereby reproducing the field relations through which habitus and shared place dispositions are formed. These findings indicate that public art contributes to place experience not only through visual or symbolic representation, but also through the interaction opportunities afforded by its physical configuration and spatial setting. The study therefore offers evidence-based implications for human-centered public-space design, socially sustainable urban regeneration, and the improvement of urban human settlements.