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This study focuses on the wooden spandrel components of Ming-style furniture to explore the application potential of generative artificial intelligence in the digital preservation and redesign of traditional woodworking cultural heritage. Based on the Dreamina AI platform, a multidimensional Prompt model integrating furniture category, form-feature, and CMF (Colour-Material-Finish) semantics was constructed. From the perspectives of material cognition and ecological reuse, a three-stage experimental path was designed: “Traditional wooden component restoration experiment—Trend CMF semantic experiment—Innovative CMF integrated redesign.” The CMF semantic experiment showed that different material and process semantic combinations had a significant impact on aesthetic and innovative perception (p<0.01), with the combination of “bamboo + green silk + phoenix embroidery” showing the best performance in terms of ecological aesthetics and cultural expression. The study concluded that generative AI under semantic control can achieve scientific and high-fidelity restoration of traditional components and extend innovative redesign through CMF semantic cultural extension. The openness and semantic construction capabilities of general generative artificial intelligence have introduced new digital expression methods to cultural heritage items made of natural materials, such as bamboo and wood. These methods are forming an interdisciplinary research paradigm that combines sustainable material restoration, cultural semantic control, and AI-driven design.

The reasonable CMF (Color, Material and Finishing) design for automotive interiors could bring positive psychophysical and affective responses of customers, providing an important guideline for automobile enterprises making differentiated products. However, current studies mainly focus on an aspect of CMF design or a single style of the automotive interior, and examined the design mainly through human visual perception. There lack systematic studies on the design and evaluation of automobile interior CMF, and more scientific evaluation of the design through human visual and touching perception was required. Therefore, this study systematically designed the automobile interior CMF based on Kansei engineering and eye-tracking technology. The study consists of five steps: (1) Product positioning: the Chinese young consumers, the new energy vehicles, and bridge and seat are the target users, the automotive model and the key interior components. (2) Kansei physiological measurement: nine groups of Kansei words and thirty-three interior samples were selected, and the interior samples were scored by the Kansei words. (3) Kansei data analysis: three design types were determined, i.e., “hard and stately”, “concise and technological” and “comfortable and safe”. Meanwhile, the CMF design elements of the automotive interiors under the three styles were obtained through mathematical methods. (4) Design practice: four CMF samples under each design style (12 samples) were developed. (5) Kansei evaluation: the design themes were conducted using eye-tracking technology, and the optimal sample that mostly satisfy the user’s Kansei requirements under each style was obtained. The proposed design process of automotive interior CMF may have great implications in the design of automotive interiors.