College of Fine Arts and Art Design, Nanyang Normal University, Nanyang 473061, Henan, China.
J Environ Public Health. 2022 Aug 27;2022:3245947. doi: 10.1155/2022/3245947. eCollection 2022.
Han painting is an important art display form in Chinese history; it has a history of hundreds of years. It is the embodiment of a higher level of Chinese painting. Han paintings can also show the development of China's political economy and culture. However, with the continuous progress of time, the patterns of Han paintings and the color characteristics of Han paintings will be greatly damaged. This limits people's research on the civilization displayed by Han paintings. At the same time, changes in the environment also have a great relationship with the integrity of Chinese painting. Therefore, the study of the impact of environmental protection on the integrity of Han paintings is crucial to the study of Chinese civilization. It is difficult for traditional research methods to discover the quantitative relationship between environmental protection and the integrity of Han paintings. In this study, the atrous convolutional neural network (ACNN) in the artificial intelligence method and the GRU method were used to explore the relationship between environmental protection and the patterns, colors, and shapes of Chinese paintings. The research results show that the ACNN method and the GRU method can better predict the patterns, shapes, and color characteristics of Chinese paintings. Through research, it can also be found that the color and pattern features of Chinese paintings contain obvious time characteristics, which requires the GRU method for feature extraction. The prediction errors of ACNN and GRU in predicting the integrity of Chinese paintings are all within 2.5%, and the largest prediction error is only 2.45%.
汉画是中国历史上一种重要的艺术表现形式，已有数百年的历史。它是中国绘画更高水平的体现。汉画还可以展示中国政治经济和文化的发展。然而，随着时间的不断推移，汉画的图案和汉画的色彩特征将受到极大的损害。这限制了人们对汉画所展示的文明的研究。同时，环境的变化也与中国画的完整性有很大关系。因此，研究环境保护对汉画完整性的影响对于研究中华文明至关重要。传统的研究方法很难发现环境保护与汉画完整性之间的定量关系。在这项研究中，使用了人工智能方法中的空洞卷积神经网络（ACNN）和 GRU 方法来探索环境保护与中国画的图案、颜色和形状之间的关系。研究结果表明，ACNN 方法和 GRU 方法可以更好地预测中国画的图案、形状和颜色特征。通过研究还可以发现，中国画的颜色和图案特征具有明显的时间特征，这需要 GRU 方法进行特征提取。ACNN 和 GRU 在预测中国画完整性方面的预测误差均在 2.5%以内，最大预测误差仅为 2.45%。
