The street spaces in tourist-oriented traditional villages served both the daily lives of villagers and the leisure activities of tourists. However, due to insufficient understanding of the spatial characteristics and under-exploration of spatial genes, these spaces often suffered from homogenization during tourism development. Thus, identifying the characteristics and connotations of such streets, understanding the relationship between tourists’ perceptions and built environment elements, and developing optimization strategies for these rural street spaces were urgent issues. Many studies have evaluated street space characteristics from tourists’ behavior, but few have focused on rural areas. Especially, research combining new technologies like artificial intelligence to study the psychological perceptions of tourists is still in its infancy. This study used a typical traditional village as a case study and applied the YOLOv5 deep-learning model to build a perception evaluation system based on three dimensions: tourists’ aggregation degree, stay time, and facial expressions. The study conducted a multivariate regression analysis on 21 factors across 4 aspects: street scale morphology, environmental facilities, ground-floor interface, and street business types. Results indicated that the functional business type of the scene had the greatest impact on tourists’ perception of the street space environment, followed by ground-floor features and environmental facilities. The regression coefficient for business in situ values and spatial perception was 0.47, highlighting it as a key factor influencing characteristic perception. Landscape water systems, flat ground-floor façades, and business diversity also positively affected tourists’ perception. This study utilized advanced techniques like the YOLOv5 model, known for its speed and accuracy, to scientifically analyze tourists’ behavior and perceptions, serving as feedback and evaluation for the village’s built environment. Empirical analysis of Yuanjia Village validated the effectiveness of the multidimensional approach and spatial gene theory. Ultimately, this method identified 12 street characteristic factors significantly affecting tourists’ perceptions. The uniqueness of this study lies in its comprehensive approach, combining empirical research, spatial gene theory, and advanced object detection technology, providing new insights for village spatial planning and construction.

Global warming has significantly intensified the risks of summer heatwaves, making outdoor thermal comfort during extreme heat periods a critical research focus. Under centralized rural village reconstruction policies, traditional settlements are being replaced by regularized modern communities characterized by new materials and standardized layouts. However, the impact of these morphological transitions on the micro-scale thermal environment remains under-researched, with a notable lack of comparative perspectives between traditional organic and modern standardized typologies. This study identifies six representative zones based on spatial configuration. By integrating UAV photogrammetry (Pix4Dmapper v4.5), AutoCAD 2019, and QGIS (v3.22), morphological characteristics were quantified, followed by microclimate simulations using ENVI-met v5.9. The results reveal that while peak daytime Physiological Equivalent Temperature (PET) in the standardized zones (49.2–51.8 °C) is slightly lower than in traditional zones (53.5–55.2 °C), a phenomenon of thermal homogenization emerges in the former. Specifically, values in standardized zones are highly concentrated around the median (53.5 °C), contributing to a significant upward trend in the minimum PET values, with nearly all sampling points exceeding 47.0 °C. Quantitative analysis identifies green coverage and perviousness as primary cooling drivers, while spatial openness and imperviousness promote thermal homogenization. In contrast, traditional zones retain critical cool refuges due to their spatial heterogeneity. This research provides an empirical foundation and quantitative reference for understanding the thermal performance differences across different rural spatial typologies. The findings offer insights for planners to optimize street layouts and shading strategies, ultimately mitigating heat stress and fostering climate-resilient modern countryside development.

The key to precise renovation of old residential areas lies in accurately determining the content to be updated. This paper focuses on the outdoor space usage needs of residents in old residential areas to clarify the key points for updating various types of outdoor spaces in old residential areas. Initially, elements related to residents' usage needs were extracted for outdoor spaces, and the Delphi method was used to establish an indicator set. Subsequently, a questionnaire meeting the KANO model requirements was designed to uncover the essential, expected, exciting, and indifferent needs of residents across several old residential areas. Based on these results and the demographic composition, K-prototype clustering analysis was employed to categorize the old residential areas into facility-supported, basic update, resource advantage, and economically adaptive types. Finally, based on the KANO analysis results and the current state of outdoor spaces in old residential areas, the characteristics of different types of old residential areas were explored, and targeted renovation suggestions for outdoor spaces were proposed, aiming to provide theoretical support and practical guidance for future renovations of old residential area spaces.