It is crucial for the protection and sustainable development of villages in China to effectively determine the changes that have occurred in traditional villages’ architectural features and the causes of such changes in order to provide timely feedback, correct protection planning, and construction in these villages. This paper analyzed quantitative indexes at three different scales in Lingquan village, namely construction land, public infrastructure, and the form, material, and color of roofs, using remote sensing images at different time points, drone shooting, and a field survey. It was found that the rate of change of the architectural features at the village and street levels were low, with an overall trend of improvement observed. However, the residential roof features at the building level showed more changes, with a trend of deterioration observed. It was also found that the active participation of the government was the main reason for the substantial improvement observed in the village’s street-level features. Further, for residential houses, the lack of standard guidance for their protection and renovation, as well as the lack of protection awareness and participation mechanisms, were the main reasons found for the deterioration of the village’s building-level features. Finally, the study forwarded some suggestions in order to provide reference value for the timely revision of protection strategies for many villages in China similar to Lingquan village.

The mode of urban renewal in China has changed from incremental expansion to a value-added stage, and the development of cities has increasingly focused on the human experience and quality improvement. However, Cultural Street Vending and the unique street spaces in the historic districts that bear important connotations to the city have shown a trend of rapid decline and even disappearance. Therefore, how to identify the characteristics and connotations of such street vending spaces, grasp the relationship between perception of locality and built environment elements in different states and formulate targeted protection and renewal strategies is presently an urgent problem to be solved. Many studies have now discussed in detail the identification of elements, street perception and preservation strategies for historic districts. However, the Cultural Street Vending space, which carries special urban memory connotations, has been neglected, especially the characterisation of this type of space in different time dimensions. This paper takes the Cultural Street Vending space as a research object and focuses on the perceptual differences between its market day and non-market day, aiming at grasping the perceptual characteristics and constituent elements of this type of street space in different time states, as well as the patterns of changes they undergo. This paper took the Baxian Temple, a typical Cultural Street Vending space in a historic district of Xi’an, as the research object and took 30 architecture students as the evaluators to score 33 pairs of adjectives and recall the elements of nine streets. This paper compared and analysed the differences in the structure of people’s psychological perception of the street in the state of market day and non-market day by SD method. It was found that the activities of the vendors obviously promoted people’s positive emotions, and three commonality factors including impression, vitality and morphosis were extracted through the factor analysis. Moreover, through the element recall method to understand the change in element perception on the market day, we found that the range of people’s perception was smaller on the market day and summarised the element map of the space and the element components of high, medium and low characteristics. The correlation analysis between psychological quantities and environmental elements revealed that people’s positive psychology has a positive correlation with vendors, goods and street components and a negative correlation with greening, colour and texture. This study can provide an important research basis for the development of conservation and renewal strategies for this type of Cultural Street Vending space.

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based...

Construction period is a core item of information for the heritage conservation of traditional dwellings, supporting building archive compilation, historical character zoning, conservation grading, and restoration strategy formulation in traditional villages. However, accurately determining the construction period of traditional dwellings remains difficult. Existing methods rely heavily on expert judgment and field surveys and are limited by low efficiency, subjectivity, and weak scalability. This study focuses on surface-built traditional dwellings in the Guanzhong region. UAV image acquisition and field photography were used to collect dwelling images, while resident interviews were used to verify construction-period information. Architectural components with chronological relevance were then annotated. On this basis, a construction-period classification model was developed using a support vector machine (SVM). Model performance was evaluated through classifier comparison, RBF parameter optimization, and repeated dwelling-level test-set evaluation. The optimized SVM model achieved an average classification accuracy of 81.9%. Qing-dynasty and post-1980 dwellings showed the highest and nearly identical classification performance, with F1-scores of 87.2% and 87.0%, respectively. Republican-period dwellings also achieved relatively good performance, with an F1-score of 82.9%, whereas dwellings built between 1949 and 1979 showed the lowest performance, with an F1-score of 75.4%. These findings indicate that construction-period classification based on architectural component features and SVM can support preliminary screening of dwelling construction periods, providing auxiliary technical support for digital archiving, period-based classification, character control, conservation planning, and targeted restoration in traditional villages.