Application of human-computer interaction system based on machine learning algorithm in artistic visual communication

Abstract After entering the 21st century, with the development of science and technology represented by artificial intelligence, the content of art design and creation is increasingly rich. Its intelligence, interactivity, and digital content expression make the relationship between science and technology, art and people more close, and also bring new opportunities for the development of interactive art. Among them, human-computer interaction is more and more widely used. With the promotion of this kind of technology, interactive art is no longer based on auditory sensory experience, but on the in-depth study of human nature and comprehensive senses. On this basis, human-computer interaction technology is introduced, thus forming a comprehensive visual art form, which communicates and expresses through strong interaction, initiative and emotion. Based on machine learning technology and human-computer visual interaction technology, this paper explores the art visual communication module based on human-computer interaction system. By analyzing the audience's line of sight and visual differences, we can get the audience satisfaction, so as to improve the expression of the work. Firstly, this paper proposes the design of human-computer interaction visual structure, and introduces the basic structure of art visual communication system, the simulation of art visual scene, the process and evaluation index of human-computer interaction recognition, visual recognition effect, etc. based on the needs of visual interaction. Finally, it analyzes the common forms of human-computer interaction in art visual communication, the reconstruction of thinking mode of art visual communication and the development direction of art visual communication. This paper believes that human-computer interaction technology plays a leading role in the development of the art field. It has a variety of functions, display methods and values. By studying machine learning algorithm and human-computer interaction system, this paper applies it to the field of artistic vision, thus promoting the development of artistic visual communication.