Colour, an art term, is an important formal element that can influence our changing feelings, and colour matching has a very important place in art. Colour is an important artistic language in the study of art, and colour is also a more attractive representation of our real world. In this paper, we fine-tune an existing mathematics model to analyze the effect of hue, luminance, saturation, and contrast on the emotion classification of art paintings and achieve an accuracy improvement of 3.4% over the current state of the art on the public dataset Twitter image dataset. Finally, we propose a pretraining strategy for a related task that significantly improves the sentiment classification task of paintings and analyze the experimental results through visual structures.
