Comparison of prediction speed.

As a UNESCO World Cultural Heritage, the aesthetic value of bronze artifacts from the Shang and Chow Dynasties has had a profound influence on Chinese traditional culture and art. To facilitate the digital preservation and protection of these Shang and Chow bronze artifacts (SCB), it becomes imperative to categorize their decorative patterns. Therefore, a SCB pattern classification method of differential evolution called Shang and Chow Bronze Convolutional Neural Network (SCB-CNN) is proposed. Firstly, the original bronze decorative patterns of Shang and Chow dynasties are collected, and the samples are expanded through image augmentation technology to form a training dataset. Secondly, based on the classical convolutional neural network structure, the recognition and classification of bronze patterns are implemented by adjusting the network parameters. Then, the initial parameters of the convolutional neural network are optimized by differential evolution algorithm, and the optimized SCB-CNN is simulated. Finally, comparative experiments were conducted between the optimized SCB-CNN, the unoptimized model, VGG-Net, and GoogleNet. The experimental results indicate that the optimized SCB-CNN significantly reduces training time while maintaining fast prediction speed, convergence speed, and high accuracy. This study provides new insights for the inheritance and innovation research of SCB patterns.

作为联合国教科文组织世界文化遗产，商周青铜器物的美学价值对中国传统文化与艺术产生了深远影响。为推动此类商周青铜器（SCB）的数字化保存与保护工作，对其装饰纹样进行分类已成为迫切需求。为此，本文提出一种基于差分进化算法的商周青铜纹样分类方法——商周青铜卷积神经网络（SCB-CNN）。首先，采集商周时期原始青铜装饰纹样，并通过图像增强技术扩充样本，构建训练数据集。其次，基于经典卷积神经网络架构，通过调整网络参数实现青铜纹样的识别与分类任务。随后，采用差分进化算法优化卷积神经网络的初始参数，并对优化后的SCB-CNN进行仿真验证。最后，将优化后的SCB-CNN与未优化模型、VGG网络（VGG-Net）、谷歌网络（GoogleNet）开展对比实验。实验结果表明，优化后的SCB-CNN在保持快速预测速度、收敛速度与较高精度的同时，显著缩短了训练时长。本研究为商周青铜纹样的传承与创新研究提供了全新视角。