Neural network’s selection of color in UI design of social software

Abstract

In recent years, the design of social software UI has become a design research focus in the field of design. Color affects many factors in UI design. However, there is currently no suitable method for effectively selecting colors in social software. In this paper, the color of social software UI design based on BP neural network is selected. The traditional BP neural network (BP), genetic algorithm improved BP neural network (GA-BP) and Mind Evolution Algorithm improved BP neural network (MEA-BP) are analyzed and summarized. Finally, the strong predictor and thought evolution method are used to improve MEA-BP-Adaboost. The experiment proves that the training results of the MEA-BP-Adaboost neural network are very good, and the color difference is reduced by about 30, 26.5 and 35.3%, respectively, compared to the three different BP neural networks. The color selection method based on MEA-BP-Adaboost can more effectively improve the accuracy of color selection in the UI design of social software, while reducing the number of experiments. In the color selection algorithm, the color accuracy rate and recall rate of the seven different colors are basically between 90 and 95%, which can basically achieve the desired effect. This also proves that the usability of BP neural network in social software UI design is very high. The methods involved in this article can be applied to other color space conversion and other image acquisition, display, processing and output devices. It is believed that these research works have certain theoretical guiding significance and practical application value to promote the development of color image color restoration technology.