The use of color elements in graphic design based on convolutional neural network model

Exploring the effective application of color elements in graphic design based on a convolutional neural network model is beneficial to promote the innovative development of the graphic design. Starting from the convolutional neural network, this paper defines the quantification of geometric features of graphic images by convolutional operation and pooling operation and uses pixel and spatial distribution for color element extraction. The global color features and local color features are used for color matching perception feature quantification, and then a graphic image design feature model is constructed, and the application analysis of color elements is carried out for this model. From the primary design color, the average selection rate of this paper’s model is 41.58%, which is 13.4% and 9.6% higher than that of the DCGAN and PLS-MLP models, respectively. From the keyword color quality generation, the color design results of graphic images have significant differences (p < 0.05). This indicates that the use of CNN-based graphic image design feature models can realize the effective application of color elements and also provides a new research direction for intelligent graphic design.