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