目的 图像分类与情感分析是当前计算机视觉领域的研究热点，为人类绘画图像数字化研究提供了有效方法，在人类绘画艺术作品保护与作品创新中具有重要的应用价值。为更好地实现绘画作品的研究与创新，本文主要针对现阶段国内外的绘画分类与情感分析相关文献，进行详细地整理与分析。方法 本文以广泛的文献研究为基础，分析中西方绘画的不同表征方式及形成原因，归纳总结支持向量机、决策树、人工神经网络和深度学习等绘画图像分类中常用机器学习方法，并概述各类方法的优劣；重点围绕绘画图像特征提取与分类，绘画情感分析两个方面，对当前文献进行了系统地分析和总结。结果 系统概括了当前绘画图像研究中常用绘画数据库；以绘画图像的笔触特征、颜色特征、形状特征和纹理特征、留白特征等方面为依据，详细综述了中西方绘画特征提取技术与分类方法的研究现状及发展；简要梳理了绘画图像分类模型中常用的评价方法，并分析了当前研究中的常用评价指标；主要从颜色特征的角度出发，阐述了西方绘画情感分析的研究进展，为中国传统绘画情感分析提供了有效的思路；最后，提出了当前绘画分类和绘画情感研究中存在的问题和挑战，并探讨了存在问题的应对之策。结论 绘画作为人类重要的文化成果，未来会涌现出更多的研究算法与探索思路，本文内容对绘画图像分类的进一步研究，特别是中国传统水墨画情感分析和绘画艺术创作方面的研究，可以起到一定的启发和指导作用。

Objective Image classification and affective analysis are popular issues in the field of computer vision, provide effective methods for digital painting, and play important roles in art protection and painting innovation. Brush stroke, colors, shapes, textures, and white spaces are important visual features of paintings. Classification based on these visual features can help identification of painting style and painter, analyze painting affection, further understand the meanings of painting creation and inherited cultures. In order to better realize the research and innovation of painting, this paper provides a comprehensive survey and analysis, focusing on domestic and international research on painting classification and affective analysis at present.Method Based on the extensive literature research, the paper firstly shows the different representation modes of Chinese and western paintings and then analyzes the reason for the differences between these two modes. The reasons are mainly derived from the various cultural backgrounds and the different ways of thinking. Chinese traditional painting has a unique artistic expression technique, which accounts for the artistic "false or true complement" effect produced by the skillful use of white space. In addition, traditional Chinese painting attaches importance to the combination of calligraphy and poetry, is decorated with seals, and emphasizes the connection between art and nature to present spirit by form. Line is the basic modeling approach and color is the auxiliary characteristic of Chinese traditional painting. The bright and dark changes of light and shadow are not emphasized. Besides, line is a form of expression of the affective characteristics of traditional Chinese painting. For an appreciator, Chinese traditional painting requires more association and imagination than mere visual effect. The Chinese painting style mainly includes two major categories, namely, traditional ink paintings and murals. The formation of the ink-and-wash style is mainly distinguished by representative painters, such as Qi Baishi, Zheng Banqiao, Xu Beihong, Wu Guanzhong, Wu Changshuo, and Huang Gongwang. Mural research is represented by mogao grottoes in Dunhuang which have a distinctive national style characterized by rich and colorful content and a form of painting that embodies the expectations of people's good wishes. Western painting is distinct from traditional Chinese painting. Traditional western painting highlights realism, similarities in appearance, reproduction, space-time, and effect of light colors. Western painting attaches great importance to the change in color, light, and shadow to portray images. In addition, the elaborate and tactful use of color can reflect painting affection. An entire painting exhibits a good sense of texture and space because of the object shading. Contrary to traditional Chinese painting, western painting provides viewers with more visual effects. The Western painting style is associated with the development of the literature and art movement, and the formation of painting style is mainly divided into baroque, three-dimensional impressionist, romantic, Rococo, and Renaissance styles. Secondly, the paper outlines the machine learning methods of support vector machine, decision tree, artificial neural network, deep learning, which are commonly used in painting classification, and analyze the advantages and disadvantages of these methods. Moreover, this paper systematically analyzes and summarizes the current literatures, focusing on the two aspects of feature extraction and classification of painting, affective analysis of painting.Result On the basis of current literatures related, this paper sums up the painting database commonly used in the present study. Moreover, it reviews in detail the research status and the development of feature extraction technology and classification methods for Chinese and western paintings in terms of such characteristics as brush strokes, color features, shape features, texture features and white features. It also briefly describes the commonly used evaluation methods of painting classification model, which are error rate and accuracy, precision and recall ratio, P-R curve and F1 measurement, and ROC curve and AUC. And several commonly used evaluation indexes in current studies are analyzed. Computer vision technology has a distinct advantage in the fields of object recognition, scene classification, image classification, and affective and semantic image analysis, which can meaningfully evaluate the perception of target images and scenes by simulating human visual ability. The essential feature of the image is the key to accurate judgement. As an image resource, the selection of painting characteristics is important in painting classification and affective analysis. Closely related to painting feature selection and classification research, machine learning is a way that investigates how to use a computer to simulate or realize people's learning activities to acquire new knowledge and skills. Machine learning is widely used in artificial intelligence. Furthermore, the paper probes the affective investigation of Western painting on the basis of color features and provides an efficient idea for the affective analysis of Chinese traditional painting. Paintings can reflect the objective social life and rich affection of their painters, and using computer intelligence to analyze the painting affection can help us understand the history and culture of various periods well. Chinese painting has a unique style in terms of ink brush stroke, ink shade, white space, and painting content, which can also convey the different affections that the painters aim to express. Combining cognitive and psychological knowledge, image feature extraction methods can also realize the affective analysis and extraction of Chinese ink-and-wash paintings. Finally, using a database of painting classification, classification goals, and the limitations of affective analysis of paintings, this study highlights the existing problems and challenges in the study of painting classification and affective analysis and discusses solutions to these existing problems.Conclusion As an important cultural achievement of human, more and more painting research algorithms and exploring thinks will emerge in the further. This article can provide guidance to further studies on painting classification, especially in the research of traditional Chinese ink painting affective analysis and painting art creation.