A Study on the Generation and Evaluation of Illustrations for Chinese Idiom Allusions Based on AIGC

As carriers of traditional culture, Chinese idiom allusions contain rich semantic and emotional content. High-quality illustrations of these idioms hold significant potential for applications in cultural communication and education. Although generative artificial intelligence has achieved substantial progress in general image synthesis, it remains challenging to produce idiom illustrations in culture-intensive scenarios that simultaneously preserve cultural symbols, maintain affective ontology, and exhibit high visual aesthetic quality. To address this gap, we propose a three-dimensional evaluation framework—Zhen-Shan-Mei (Truth-Goodness-Beauty)—for idiom illustrations. The ‘Truth’ module uses Chinese vision–language models to quantify cultural symbols; the ‘Goodness’ module applies cross-modal affective analysis to assess affective ontology; and the ‘Beauty’ module computes quantitative aesthetic metrics (composition balance, color harmony, and line expressiveness). Based on this system, an AI-idiom prototype system is constructed to realize closed-loop iteration of generation-evaluation-regeneration and threshold screening. Experiments show that the proportion of illustrations selected by subjects after the “Truth-Goodness-Beauty” screening reaches 78.1%. The results suggest that the proposed method is effective in maintaining cultural symbols, strengthening affective ontology, and improving visual aesthetics and offers a potentially interpretable and reproducible evaluation and optimization framework for culture-intensive image generation tasks.