The spatial forms of hill-neighboring blocks and the wind–thermal environment are crucial components of urban planning and urban ecology. Understanding their correlation is significant for creating a healthy community and enhancing the sustainable level of buildings and their systems. Therefore, Jinan, a multi-mountainous city in China, is taken as the research area, and the ideal model of hill-neighboring blocks is built based on categories of all these block types by aerial imagery and a semantic segmentation algorithm. Then, we use the CFD simulation software PHOENICS and the evaluation system weighted by random forest to simulate and assess the wind–thermal environment. Ultimately, the correlations and specific mathematical equations between the spatial form indicators and wind–thermal environment are obtained by a parametric method. The results demonstrate that the interface density on the near-hill side and hill surface roughness are the most related indicators to the overall wind–thermal environment. The variation in block spatial form has the most distinct influence on the proportion of breeze area and PMV. The relationship determined herein can provide strategic recommendations for decision makers for optimizing the outdoor air flow of blocks and enhancing the thermal comfort of pedestrians, which helps to create a healthy and comfortable outdoor environment in multi-mountainous cities.

Coastal traditional rural settlements face increasing challenges from human activities and natural disasters driven by global climate change and rapid urbanization. Ensuring their spatial stability is essential for ecological security, economic development, and social sustainability. This study addresses the lack of unified methodologies for assessing the resilience of regional traditional village clusters by proposing a “network construction–spatial simulation–resilience assessment” framework based on complex network theory. Using the Haicao housing village cluster in Rongcheng City, China, as a case study, a spatial network model was constructed, and resilience was evaluated under both current and future scenarios using eight resilience indicators. The results show that the current spatial network structure exhibits clustering with weak interconnections among subgroups. Key nodes significantly influence network metrics, resulting in low overall resilience. In future scenarios, protective measures targeting the top 15% of villages with high development potential enhanced social connections, reduced reliance on key nodes, and improved network resilience. This study provides a quantitative method for assessing the resilience of traditional village clusters and identifies critical pathways and nodes to optimize regional spatial structures. The findings offer new perspectives for guiding the preservation and sustainable development of traditional villages.