Research on painting image classification based on convolution neural network

The digitalization of painting works is of great significance to the effective use of painting resources. Traditional image classification methods do not consider the subjective characteristics of painting works, and most of the features need to be manually extracted. There are problems such as loss of detail features. In this paper, a painting image classification method based on convolution neural network is proposed, and the influence of the size of convolution kernel, the structure width of convolution neural network, and the number of training samples on the classification results is analyzed to optimize the network structure and parameters. The experimental results show that the method is effective for the classification of painting images, and the classification results of different painting image data sets are also good.