Rapid urbanization and spatial constraints in high-density residential areas pose significant challenges to public health and well-being. This study investigates the mechanisms by which the visual environment of urban micro public spaces shapes residents’ psychophysiological responses to encourage spontaneous physical activity and advance active health. Using machine learning and semantic segmentation, 9 core visual elements across 20 micro public space scenes in high-density urban neighborhoods were quantified. An immersive virtual reality (VR) experiment was conducted, collecting synchronized multimodal psychophysiological data from 60 participants, which yielded 600 valid observations. Through an analytical framework combining Self-Organizing Map (SOM) clustering and Random Forest (RF) modeling, three distinct functional archetypes were identified: Restoration-Supporting, Activity-Promoting, and Stress-Inducing. The Activity-Promoting archetype was most effective in fostering spontaneous activity intention, characterized by a high proportion of activity areas, a moderate sky view factor, and minimal physical barriers. RF modeling further pinpointed pedestrian density, activity area ratio, and green space ratio as key visual drivers of health-promoting outcomes. Based on these findings, a “Visual Activation for Active Health” framework is proposed. It posits that moderate visual-environmental stimulation is the core mechanism for transforming passive spaces into health-promotive settings, thereby establishing a theoretical foundation for the evidence-based design of healthy and sustainable urban environments.

The continued digitization of open museum collections provides new opportunities for the intelligent organization and visual discovery of cultural heritage. However, morphological similarity between ceramic forms, intra-class variation, and changing photographic conditions remain challenges for automated classification, model interpretation, and similar-object retrieval. This study uses 3305 Chinese ceramic objects from the open collection of The Metropolitan Museum of Art (The Met) to develop an explainable and traceable workflow for ceramic-form classification and visual retrieval. Museum metadata were standardized into 13 form categories, from which 2755 objects were used to establish a seven-class primary classification task. Explicit morphological features, handcrafted visual features, ResNet50 representations, DINOv2 representations, and morphology–deep feature fusion were evaluated under a unified data split and evaluation protocol. Explainable artificial intelligence (XAI) methods were further used to examine spatial model responses and feature attributions of explicit morphological variables, while different representations were evaluated for content-based visual retrieval. The results show that deep visual representations effectively support ceramic-form classification, with DINOv2 demonstrating comparatively stable performance across multiple random seeds. Morphology–deep feature fusion did not provide a consistent classification advantage over DINOv2-only, but the fused representation showed clearer complementary value in visual retrieval, achieving the highest Precision@5 (0.819) and mean average precision at 10 (mAP@10; 0.773). XAI analyses further indicated that structurally meaningful spatial responses and explicit geometric descriptors contributed to form discrimination. By linking classification, interpretation, and retrieval outputs to Object IDs and original collection records, the proposed workflow provides a practical computational approach for ceramic-form organization, similar-object discovery, and traceable visual retrieval in digital museum collections.