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Digitalization of Zhejiang Yueqing Fine-Paper-Cutting Empowered by Artificial Intelligence: Integrated Application of F-AHP and RIME-BPNN Algorithms

By: Xiaer Zhang 1, Wenjing Liu 2
1School of Art and Design, Ningbo University of Finance and Economics, Ningbo, Zhejiang, 315175, China
2College of Packaging Design and Art, Hunan University of Technology, Zhuzhou, Hunan, 412007, China

Abstract

The preservation and modernization of traditional paper-cutting art face significant challenges due to its reliance on manual craftsmanship and subjective evaluation. This study proposes a computational framework integrating fuzzy analytic hierarchy process (F-AHP) and a Rime Optimization Algorithm-Back Propagation Neural Network (RIME-BPNN) to digitize and op-timize the design process for Yueqing intricate paper-cutting. First, we formalize the design op-timization problem as a multi-criteria decision-making task, where F-AHP quantitatively extracts key emotional dimensions (“Exquisite-Rough”, “Modern-Traditional”, “Elegant-Rustic”) from both artists’ expertise and consumer preferences. Second, we introduce RIME-BPNN, a metaheuristic-enhanced neural architecture that demonstrates superior prediction performance over conventional BPNN and SVR models through adaptive parameter optimization. Third, we implement a Generative Adversarial Network (GAN) that automatically generates design solu-tions by learning the mapping between F-AHP-derived parameters and visual features. Quanti-tative evaluations demonstrate the framework’s effectiveness: user studies show higher emo-tional resonance scores and greater satisfaction compared to conventional methods. The pro-posed system’s key innovations include: (1) a datadriven F-AHP method bridging subjective art evaluation with computable metrics, (2) RIME-BPNN’s superior convergence in modeling non-linear aesthetic preferences, and (3) an end-to-end pipeline from perceptual analysis to AI-generated design. This work provides a scalable computational paradigm for intangible cul-tural heritage digitization, demonstrating how hybrid AI techniques can address challenges in traditional art preservation and innovation.