A planning method for traditional villages based on natural language processing and geographic information systems.

In the context of China's rural revitalization, preventing the misallocation of national resources and identifying villages in urgent need of renewal have become critical goals. Existing planning methods often overlook how emotional perceptions influence environmental awareness and spatial planning. This study proposes an innovative framework to address this gap, focusing on optimizing village construction to enhance emotional responses. By using web crawling and natural language processing (NLP), social media data is analyzed to assess emotional evaluations. Geographic information systems (GIS) are then applied to evaluate village layouts and environmental characteristics. Focusing on national-level traditional villages in Weinan, Shaanxi Province, the study identifies "exemplary," "inefficient," and "priority" villages, offering targeted planning recommendations. Results show: (1) emotional differences across population groups; (2) varying impacts of landscape elements, with the sky having the strongest positive influence (r = 0.328, p = 0.001); and (3) "exemplary," "priority," and "inefficient" villages account for 6.52%, 39.13%, and 50.00%, respectively, revealing a need for timely renewal. This study offers a novel theoretical and practical framework for integrating emotional analysis into China's rural planning to ensure more effective resource allocation and village development.