Nomadic heritage villages constitute significant material cultural heritage. Under China’s cultural revitalization and rural development strategies, these villages face spatial degradation driven by tourism and urbanization. Current research predominantly employs isolated analytical approaches—space syntax often overlooks social dynamics while social network analysis (SNA) overlooks physical interfaces—hindering the development of holistic solutions for socio-spatial resilience. This study proposes a multi-scale integrated assessment framework combining social network analysis (SNA) and space syntax to systematically evaluate public space structures in traditional nomadic villages of Xinjiang. The framework provides scientific evidence for optimizing public space design in these villages, facilitating harmonious coexistence between spatial functionality and cultural values. Focusing on three heritage villages—representing compact, linear, and dispersed morphologies—the research employs a hierarchical “village-street-node” analytical model to dissect spatial configurations and their socio-functional dynamics. Key findings include the following: Compact villages exhibit high central clustering but excessive concentration, necessitating strategies to enhance network resilience and peripheral connectivity. Linear villages demonstrate weak systemic linkages, requiring “segment-connection point supplementation” interventions to mitigate structural elongation. Dispersed villages maintain moderate network density but face challenges in visual integration and centrality, demanding targeted activation of key intersections to improve regional cohesion. By merging SNA’s social attributes with space syntax’s geometric precision, this framework bridges a methodological gap, offering comprehensive spatial optimization solutions. Practical recommendations include culturally embedded placemaking, adaptive reuse of transitional spaces, and thematic zoning to balance heritage conservation with tourism needs. Analyzing Xinjiang’s unique spatial–social interactions provides innovative insights for sustainable heritage village planning and replicable solutions for comparable global cases.

Under intensifying global warming and extreme climate events, the climate adaptability of folk houses in Xinjiang’s arid regions faces critical challenges. However, existing studies predominantly focus on traditional folk houses under current climate conditions, neglecting modern material hybrids and long-term performance under future warming scenarios. This study develops a data-driven framework to assess and enhance building envelope performance across historical-to-future climate conditions (2007–2021 TMY data, 2024 observations, and 2050/2080 SSP3–7.0 projections) using the entropy-weighted TOPSIS method and NSGA-II algorithm. Analyzing rammed earth, brick–wood, and brick–concrete folk houses in Kashgar, Hotan, Kuqa, and Turpan, the optimization targets thermal discomfort hours (TDHs), heating energy consumption (HEC), and net present value (NPV). The results demonstrate optimized solutions achieve 30–60 year climate resilience, reducing HEC by 51.54–84.76% (43.02–125.78 kW·h/m²·a) compared to baseline buildings, TDH by 15–52.93% (301–1236 h) in arid Zone A and by 5.54–10.8% (208–352 h) in the extreme hot-arid Zone B (Turpan), and NPV values by CNY 31,000–85,000. Rammed earth constructions demonstrate superior performance in Zone A, while brick–concrete exhibits optimal extreme hot-arid adaptability, and brick–wood requires prioritized retrofitting. The findings advocate revising China’s design standards to address concurrent winter overcooling and summer overheating risks under future warming. This work establishes a climate-resilient optimization paradigm for arid-region folk houses, advancing energy efficiency and thermal comfort.