Traditional villages (TVs) are clusters for the conservation of farming civilization heritage with multiple values. Studying their spatial distribution and driving mechanisms is conducive to formulating conservation and development strategies. In this study, 777 TVs in Yunnan Province were examined. Spatial analysis methods such as the nearest neighbor index (NNI), kernel density estimation (KDE), and Moran’s I were used to investigate their spatial distribution patterns. Twelve driving factors were selected from natural, spatial, social, and cultural aspects. The driving mechanisms affecting the distribution of TVs were explored by employing the geographically weighted regression (GWR) model and Geodetector combined with mathematical and statistical methods to systematically study the spatial autocorrelation and heterogeneity of TVs in Yunnan. The results of this study indicate that: (1) The 777 TVs in Yunnan Province are evidently clustered in the overall distribution, presenting a contiguous and concentrated distribution pattern of “3 high-density areas + multiple medium-density belts”. However, the distribution of TVs is unbalanced. The spatial distribution is significantly positively correlated globally, and there exist three types of clustering in local areas. (2) The spatial layout of TVs in Yunnan Province is jointly influenced by multiple factors, with obvious spatial heterogeneity. Regional cultural factors are the key ones. TVs tend to be distributed in areas with medium–high elevation, flat slops, sunny slopes, moderate precipitation and temperatures, and a certain distance from water sources and roads. A large number of TVs are distributed in areas with a high proportion of ethnic minorities. (3) The main factors affecting the distribution of national-level TVs in Yunnan are intangible cultural heritage, cultural relic protection units, followed by factors such as transportation, slope, and elevation. The interaction between intangible cultural heritage (X11) and cultural relic protection units (X12) reveals the strongest interactive driving force. This study reveals the diverse characteristics of the spatial distribution of national-level TVs in Yunnan and their influencing mechanisms, which can provide a scientific decision-making basis for the future protection and development of TVs in Yunnan and guide the sustainable development of TV cultural heritage clusters.

The mountainous regions of southwest China represent one of the world’s most distinctive and sensitive areas. Against the backdrop of rapid urbanization and water conservancy construction, rural landscapes in these regions face challenges such as fragmentation, homogenization, and loss of local distinctiveness. Responding to the initiative of the European Landscape Convention (ELC), this study takes the Longchuan River Basin in Southwest China as a case study, and constructs a rural Landscape Character Assessment (LCA) framework adapted to the multi-level governance system. We established a multi-scale evaluation system covering large scale (county-level), medium scale (township-level), and detailed scale (reservoir area-level). The large scale integrated 6 categories of natural variables, while the medium scale involved 4 categories of natural variables and 4 categories of cultural variables. Using a Principal Component Analysis–Two-Step Clustering coupled method and eCognition software, landscape character types and areas were identified respectively. The results show that 11 landscape character types and 41 landscape character areas were identified at the large scale, and 6 landscape character types and 73 landscape character areas at the medium scale. At the detailed scale, 4 typical reservoir areas were selected for field surveys, which verified the in-depth impact of hydropower construction on landscape characteristics. The study provides a transferable technical pathway and policy recommendations for monitoring and managing rural landscapes in mountainous regions. Supports the long-term sustainability and resilience of rural landscapes in China.