Quantitative Evaluation of Intangible Cultural Heritage Inheritance: Stroke Trajectory Analysis and Machine Learning Model Construction for Paper-Cutting Craftsmanship

With the acceleration of globalization, intangible cultural heritage (ICH) faces new challenges, particularly in the crisis of craftsmanship inheritance. Effectively quantifying and assessing artisans skills is crucial for preserving traditional craftsmanship. Therefore, this study proposes an innovative approach based on papercutting art, utilizing stroke trajectory analysis combined with machine learning models to quantitatively evaluate the skills of papercutting inheritors. Specifically, high-quality stroke trajectory images of papercutting art are collected using web crawling techniques. Degraded images are then generated through threshold processing and pixel swapping, followed by the use of a diffusion model to synthesize defective stroke trajectory images. Based on these data, we design the BruNet model, which extracts multi-space features through convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs). These features are then fused using a feature fusion module and trained with cross-entropy and contrastive learning losses. Experimental results demonstrate that BruNet achieves significant performance improvements over state-of-the-art models, providing novel insights and technical support for ICH preservation.