Integration and development of modern art design and environmental design in the context of deep learning

Abstract

In the rapid development of digitalization, the combination of artificial intelligence technology and environmental art design is expected to gradually change the design process and promote the sustainable development of design. After specifically exploring the application of deep learning technology in the integration of modern art and environmental design, this paper utilizes the generative adversarial network under the deep learning model to design the intelligent color-matching model integrating visual aesthetics and the intelligent environmental art style generating a model based on CycleGAN, respectively and conducts the perceptual intention analysis of the environmental art design works under the intelligent generation to explore its developmental progression. The results of the eye movement experiment show that the correlation coefficients between the perceptual imagery score data of the four groups of environmental art designs are all in the range of 0.433 to 0.599, with a moderate positive correlation. Among them, the correlation coefficients between the total access time, gaze duration, gaze number, and perceptual imagery score are close to 0.6, and the significance coefficients are all less than 0.05, which indicates that the automatic generation of modern art design and environmental design proposed in this paper has a better user aesthetic preference. Introducing AI-assisted sustainable design can ensure the sustainability and effectiveness of the design solutions in practical applications, thus promoting the entire environmental art design industry to develop in a greener and more sustainable direction.

Keywords

• deep learning model
• generative adversarial network
• intelligent color matching
• art style generation
• art environment design
• 68m10