Generative AI-Driven Optimization for Cultural Packaging Design: Translating Chinese Poetic Imagery into Tea Packaging Design

Abstract: The rapid advancement of AIGC has unlocked new potential in design processes, making it an essential tool for innovation. However, systematic research on AIGC-based product packaging design remains insufficient, particularly in enhancing design efficiency and accurately reflecting cultural elements. This study proposes an AIGC-based optimization framework to address these challenges. First, ChatGPT and the LDA model were used to extract imagery words from high-quality literary works aligned with the design theme. These words served as prompts for ChatGPT, guiding iterative image creation through Midjourney. Furthermore, AIGC-based image recognition was integrated to incorporate big data into the decision-making process. To ensure cultural relevance and consumer satisfaction, the AHP and FCE methods were employed to conduct a multidimensional evaluation and optimization. The empirical findings from the case study employing The Thousand Poems as thematic content for Jiangnan Longjing tea packaging design substantiate that AI-generated content driven design methodologies not only optimize decision-making efficacy but also establish an adaptive framework for design refinement, thereby providing a robust theoretical and practical foundation for subsequent scholarly inquiry.

Keywords: AIGC, packaging design, text extraction, Chinese poetic aesthetics

DOI: 10.54941/ahfe1006930

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