The Optimization of Color and Texture for Electronic Products Based on Collaborative Cognition between Human and Agent

In the prediction of the color-texture optimization configuration of children's mobile phone Lily based on human intelligence-collaborative agile industrial design model, in order to accurately obtain the cognitive preferences of color-texture and ensure the accuracy of machine learning, the hesitant fuzzy Kansei-TOPSIS evaluation method and semantic difference method are used to analyze the cognitive preferences of colortexture information samples sprayed by spindle automatic painting, which are transformed into multimodal information representation coding of machine language, graphics, and rendering effects. This is done through inputting into the AIGC (Artificial Intelligence and General Computing) agile industrial design model for prediction, comparing with the results of the machine learning case set, and demonstrating that the method of obtaining cognitive preferences for color-texture can accurately refine multimodal information representation coding.