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.

Nominally optimized atrium designs may lose reliability when geometric and envelope parameters deviate during implementation. Extending a previously established deterministic surrogate-assisted workflow, this study investigates uncertainty-aware design decision-making for a multi-atrium commercial building in Urumqi, China. The severe-cold climate is treated as a fixed environmental boundary condition, while uncertainty analysis is restricted to six implementation-related design variables. A Honeybee–EnergyPlus model supported by short-term field calibration was used to generate a Latin-hypercube dataset, and CatBoost surrogate models were developed for predicted percentage of dissatisfied (PPD), daylight autonomy at 300 lx (DA), and summer–winter solar-radiation difference (D-RAD). Sobol indices and SHAP were used to interpret parameter importance and interaction effects. Monte Carlo propagation of the six uncertain variables was embedded within NSGA-II using mean–variability robust objectives, and TOPSIS was applied to select representative solutions for subsequent space-syntax evaluation. The CatBoost models achieved global holdout test-set R² values of 0.990–0.994. Compared with the deterministic compromise solution, the robust solution shifted toward a different design configuration while maintaining broadly comparable mean performance. The uncertainty-adjusted DA and D-RAD objectives improved by approximately 2.21% and 6.20%, respectively, whereas the PPD objective deteriorated marginally by approximately 0.25%, reflecting the multi-objective trade-off inherent in the robust formulation. Expanded high-fidelity re-evaluation of ten Pareto-region designs revealed larger local errors for DA and D-RAD than suggested by the global holdout statistics, although the comparative performance ranking was strongly preserved. The results indicate that implementation-related design-variable uncertainty can alter the preferred design region and the balance among competing environmental objectives. Environmental robustness and spatial-configuration quality should therefore be treated as complementary criteria, with space-syntax analysis serving as a post-optimization screening tool rather than a direct measure of user experience.