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.

Since the reform and opening-up, China’s urbanization has progressed rapidly, leading to a continuous migration of rural populations to urban areas. This population outflow is particularly pronounced in the economically less developed Northwest China, triggering a series of issues such as rural vacancy and the idling of residential resources. Against this backdrop, there is an urgent need for scientific methods to guide the renewal design of rural residences, aiming to enhance living comfort, optimize spatial utilization efficiency, and curb rural decline. Although existing research often explores resource utilization strategies at the village level, systematic studies focusing on the individual building scale remain relatively scarce. This study targets rural residences in the Hexi Corridor region. It systematically identifies the “Seasonal Room Rotation” living pattern formed under the context of population contraction and analyzes the “conflict between solar gain and overheating” phenomenon caused by the extreme climate. By integrating architectural characteristics and psychrometric chart analysis, suitable passive design strategies are summarized. Furthermore, based on objectives for indoor light environment and thermal comfort, a genetic algorithm is employed to conduct multi-objective optimization of various building parameters. The results indicate an inherent contradiction in achieving both “warm in winter and cool in summer” within a single room. However, by functionally differentiating building spaces according to their season of use—designating separate “Winter Rooms” and “Summer Rooms”—both winter thermal insulation and summer cooling performance can be systematically enhanced. The research further proposes key design parameters applicable to this new “Seasonal Room Rotation” living pattern, including courtyard form, building height, window-to-wall ratio, and shading component dimensions. This elevates the seasonal adaptation strategy from an internal room-level compromise to a holistic building-level allocation of spatial resources. This study constructs a design methodology for enhancing the green performance of rural residences amidst population contraction. It simultaneously optimizes indoor comfort and spatial utilization efficiency, offering a highly operable new design paradigm for the green renewal of rural homes in complex climatic conditions.

Guanzhong region of Shaanxi province has a continental monsoon climate of warm temperate zone, with hot in summer and cold in winter. Therefore, the strategies of traditional houses dealing with wind environment are mainly ventilation in summer and wind protection in winter. Due to the limitation of technical and economic conditions, winter heating has always been the core problem of rural houses, so people usually adopt a relatively closed space layout to strengthen the windproof performance of buildings, but also lead to poor indoor ventilation in summer.With the development of social economy, rural residents have higher requirements for living quality, so it is very important to improve ventilation efficiency in summer under the condition of ensuring wind protection in winter. Taking the red brick house project in Ezi village of Xianyang as a practical case, in order to improve indoor wind pressure ventilation and thermal pressure ventilation efficiency, the project introduces double-height space and skylight that commonly used in urban buildings into rural houses, providing reference for the construction of similar residential buildings in Guanzhong area.