基于感性意象和AIGC的高铁客室风格设计研究OA

To reveal the characteristics of users'kansei image regarding intelligent high-speed rail cabins and their association with travel preferences,thereby constructing a differentiated design framework.A dual text corpus was constructed.Techniques including word frequency analysis,user surveys,Semantic Differential(SD)method,and factor-cluster analysis were employed to extract kansei image vocabulary and map it to user preferences.AI-generated designs were validated using a LoRA model.Based on corpus mining and large-scale user surveys,seven pairs of core kansei style labels were refined,representing the association between user aesthetic preferences and travel scenarios.A differentiated design framework was constructed.The study demonstrates that the kansei image-driven design framework,integrated with AIGC technology,significantly enhances the precision and generation efficiency of design solutions.It provides a data-driven innovative paradigm to address issues such as ambiguous aesthetic positioning and lagging user demand response in intelligent cabin design,effectively promoting the synergistic optimization of functionality and aesthetics.