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<p>As generative AI becomes embedded in cultural heritage visualization and design, it is crucial to understand how audiences evaluate AI-generated heritage images in terms of cultural continuity and innovation. This study investigates how cultural memory, design semiotics, and the Function–Behavior–Structure (FBS) framework jointly shapes perceived cultural heritage digital inheritance and innovation (CHDII), using AI-generated Huizhou woodcarving images as a case. We propose a Cultural Memory–Semiotics–FBS model in which material, functional, and symbolic memory dimensions influence semiotic sign types (icon, index, symbol), FBS constructs, and CHDII. Theory-driven modular prompts were used in Midjourney to generate a standardized set of 20 images, followed by a survey of 434 respondents. Structural equation modelling and multilayer perceptron neural networks were applied to test nine hypotheses and explore potential nonlinear effects. Results reveal two dominant pathways: FD–IN–F–B–S and SD–SY–S, while the material/iconic path is not significant. ANN analyses confirm the central roles of functional, behavioural, and symbolic variables. The findings position generative AI as a controllable medium for encoding cultural meaning and offer a reusable workflow for culturally informed digital heritage design.</p>

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A cultural memory semiotics and function behavior structure model for digital inheritance and innovation in AI generated Huizhou woodcarving images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Yanran Qian,
  • Qian Bao,
  • Shengxin Zhang,
  • Xiaojie Peng

摘要

As generative AI becomes embedded in cultural heritage visualization and design, it is crucial to understand how audiences evaluate AI-generated heritage images in terms of cultural continuity and innovation. This study investigates how cultural memory, design semiotics, and the Function–Behavior–Structure (FBS) framework jointly shapes perceived cultural heritage digital inheritance and innovation (CHDII), using AI-generated Huizhou woodcarving images as a case. We propose a Cultural Memory–Semiotics–FBS model in which material, functional, and symbolic memory dimensions influence semiotic sign types (icon, index, symbol), FBS constructs, and CHDII. Theory-driven modular prompts were used in Midjourney to generate a standardized set of 20 images, followed by a survey of 434 respondents. Structural equation modelling and multilayer perceptron neural networks were applied to test nine hypotheses and explore potential nonlinear effects. Results reveal two dominant pathways: FD–IN–F–B–S and SD–SY–S, while the material/iconic path is not significant. ANN analyses confirm the central roles of functional, behavioural, and symbolic variables. The findings position generative AI as a controllable medium for encoding cultural meaning and offer a reusable workflow for culturally informed digital heritage design.