Application of Deep Neural Network and Human-Computer Interaction Technology Based on Multimodal Perception in Art Design

Abstract

The essence of art design is the process of emotional interaction with humans. In this paper, emotions are classified using multimodal fusion features, and abstract fusion features are obtained by sampling the multimodal matching tensor using average pooling. The matching fusion matrix in the tensor operator is used to convert from two-modal to multi-modal matching. Virtual interaction model in art design controls the design objectives, and the optimized virtual world is constructed by using virtual reality technology, so as to build an immersive art design model. Finally, a study was conducted to examine the impact of the use of emotion perception and interaction technology in art product design with students from the School of Design and Art, University of G. The results show that the liking degree of three-color matching in the cognitive experiment test is 0.307, which is higher than the liking degree of two-color matching of 0.223, indicating that overall three-color matching samples are more popular in art design. This study provides effective evaluation and guidance for designing art products that are emotional and innovative.