Focusing on the innovative path of integrating artificial intelligence generated content (AIGC) technology into the cultural and creative design of Mianzhu New Year Paintings, a national intangible cultural heritage, the work aims to explore the mechanism of cultural gene extraction and recreation of traditional art under digital and intelligent empowerment, and promote the digital inheritance and value regeneration of intangible cultural heritage. Based on the cultural gene theory and combined with digital humanities research methods, an "AIGC-based intangible cultural heritage elements deconstruction-reconstruction" model was proposed. By establishing multimodal data of Mianzhu New Year Painting patterns and adopting the process of "cultural feature semantic analysis—digital generation algorithm iteration—multi-scenario design output", the diffusion model realized the intelligent extraction and translation of cultural symbols, and completed the dynamic migration and adaptation of traditional art styles. AIGC technology could break through the experience dependence of traditional design. Through the digital translation of cultural genes, it realized the model transformation of Mianzhu New Year Paintings from symbol collage to reconstruction and from material carriers to digital experiences. At the practical level, the study produces multiple forms of cultural and creative designs and enhances the communication vitality of New Year Painting culture among contemporary audiences. At the theoretical level, it provides methodological support for AIGC technology to activate the digital innovation of traditional intangible cultural heritage art, offers new technical paths for the intelligent development of intangible cultural heritage cultural and creative industries, and provides a theoretical reference for the innovative transformation of traditional culture in the digital economy era.
Key words
digital intangible cultural heritage /
Mianzhu New Year Paintings /
cultural and creative design /
artificial intelligence generated content (AIGC)
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