Abstract: This paper discusses the process of Chinese ink-painting style learning using convolution
neural network. Firstly, the frame structure of VGG19 neural network model is analyzed, and the
process of using VGG19 model to separate and recombine the content and style of artistic images.
Secondly, based on the theory, according to the actual characteristics of Chinese ink painting, the
appropriate choice of the convoluted layer to process the content image is found and proved by
experimental results. The optimal combination of convoluted layer to extract the style from Chinese
ink painting is also found by experiment, and the criteria for visual evaluation of image quality are
proposed. Finally, by adjusting the proportion coefficient of the content image and the style image,
the expected combined image can be obtained, which verifies the feasibility of the theory and puts
forward a new method for Chinese ink-painting style extraction.

Key words: convolutional neural network, Chinese ink-painting, artistic style learning, feature
extraction