Optimizing the wind environment within university dormitory areas is essential for ensuring student safety, enhancing living comfort, and improving building energy efficiency. In this study, the wind environment of multi-story university dormitories in cold regions is comprehensively investigated through computational fluid dynamics (CFD) simulations conducted with the PHONECIS software (version 2019), combined with orthogonal experimental design methods for systematic analysis and optimization. Through orthogonal experimental design, the effects of key morphological parameters—including building layout, length, width, and height—on the near-ground wind environment were evaluated. Among these, building width exerted the greatest influence, followed by building length, layout form, and finally building height. Based on the analysis, the optimal design scheme features a staggered building layout, with individual dormitory buildings measuring 60 m in length, 16 m in width, and 11.4 m in height. This optimized design was implemented in the multi-story dormitory area of the eastern section of Chang’an University’s New Campus. A comparative analysis of wind speed distribution before and after optimization, conducted specifically for the outdoor spaces during the winter season, revealed that the average near-ground wind speed was reduced from 3.3 m/s to 2.7 m/s, achieving an 18% reduction. The staggered arrangement and adjusted building proportions effectively dispersed airflow, mitigated high-velocity zones, and significantly enhanced outdoor wind comfort and pedestrian safety. This study introduces a morphology–wind environment coupling strategy from an architectural perspective to guide the design of dormitory buildings in cold regions. Rather than focusing on mathematical modeling, the research emphasizes design-oriented outcomes aimed at informing and optimizing practical architectural solutions for safer, more comfortable, and energy-efficient campus living environments.

The integration of virtual reality, digital twins, and spatial behavior-tracking technologies is reshaping cultural exhibition architecture, shifting the design focus from functional efficiency to immersive, user-centered experiences. However, the behavioral dynamics within these interactive environments remain insufficiently addressed. This study proposes a behavior-oriented spatial typology grounded in Bitgood’s attention–value model, which maps the psychological stages—Attraction, Hold, Engagement, and Exit—onto four spatial categories: Threshold Space, Transitional Space, Narrative Focus Space, and Closure Space. Each represents a distinct phase of perceptual and behavioral response along the exhibition sequence. A mixed-method approach was employed, combining eye-tracking experiments with structured questionnaires to capture both physiological reactions and subjective evaluations. Key spatial variables—enclosure (X₁), visual corridors (X₂), spatial scale (X₃), and light–shadow articulation (X₄)—were analyzed using multiple regression to assess their impact on interest and dwell time. The results show that enclosure (α = −0.094; β = −0.319) and light–shadow articulation (α = −0.057; β = 0.156), respectively, decreased interest and increased dwell time, while spatial scale (α = 0.042; β = 0.186) positively affected both. Visual corridors had minimal influence (α = −0.007; β = 0.022). These spatial effects align with the proposed typology: Threshold Spaces support rapid orientation and exploratory behavior, while Transitional Spaces aid navigation but reduce sustained attention. Narrative Focus Spaces enhance cognitive engagement and decision making, and Closure Spaces foster emotional resolution and extended presence. These findings validate the proposed typology and establish a quantifiable link between spatial attributes and visitor behavior, offering a practical framework for optimizing immersive exhibition sequences.

The integration of virtual reality, digital twins, and spatial behavior-tracking technologies is reshaping cultural exhibition architecture, shifting the design focus from functional efficiency to immersive, user-centered experiences. However, the behavioral dynamics within these interactive environments remain insufficiently addressed. This study proposes a behavior-oriented spatial typology grounded in Bitgood’s attention–value model, which maps the psychological stages: Attraction, Hold, Engagement, and Exit—onto four spatial categories: Threshold Space, Transitional Space, Narrative Focus Space, and Closure Space. Each represents a distinct phase of perceptual and behavioral response along the exhibition sequence. A mixed-method approach was employed, combining eye-tracking experiments with structured questionnaires to capture both physiological reactions and subjective evaluations. Key spatial variables: enclosure, visual corridors, spatial scale, and light–shadow articulation—were analyzed using multiple regression models to evaluate their effects on interest levels and dwell time. Results show that Threshold Spaces support rapid orientation and exploratory behavior, while Transitional Spaces aid navigation but reduce sustained attention. Narrative Focus Spaces enhance cognitive engagement and decision-making, and Closure Spaces foster emotional resolution and extended presence. These findings validate the proposed typology and establish a quantifiable link between spatial attributes and visitor behavior, offering a practical framework for optimizing immersive exhibition sequences.

Brownfield-transformed urban parks, particularly those derived from industrial heritage, play a critical role in both cultural preservation and public-space provision. However, existing studies often rely on linear models and general urban contexts, limiting their ability to capture nonlinear, interaction-driven perception and translate analytical results into design-oriented insights. To address this gap, this study develops an interpretable data-driven framework integrating NLP (natural language processing) with explainable machine learning. Using social media reviews from Shougang Park in Beijing, built environmental elements are identified and structured into four dimensions—Accessibility, Safety, Comfort, and Enjoyment. An XGBoost model combined with SHAP analysis is employed to examine variable importance, nonlinear relationships, and interaction effects. The results reveal that visitor satisfaction is governed by heterogeneous and nonlinear relationships rather than independent additive effects. Several variables exhibit threshold-like, diminishing, and inverted-U-shaped patterns, indicating sensitivity to intensity ranges. More importantly, spatial perception emerges from the nonlinear coupling of multiple elements, forming four representative interaction types: compensatory, inverted-U-shaped, context-dependent, and threshold-like relationships. Key interactions are concentrated around industrial landscape, leisure activities, and supporting facilities. Building on these findings, the study translates interactions into design-oriented strategies, emphasizing synergistic configuration, functional balance, moderated development intensity, and context- sensitive programming. By linking interpretable machine learning with spatial design, this research advances an interaction-oriented paradigm and provides a transferable framework for satisfaction-informed evaluation and optimization of brownfields.