Application of Intelligent Assisted Technology in Green Environmental Art Design and Analysis of Sustainable Paths

Abstract

The integration of intelligent design technologies with sustainable environmental planning requires effective multimodal information processing and adaptive optimization mechanisms to improve ecological performance while maintaining design quality. This paper proposes an intelligent ecological design framework based on a multimodal generative adversarial network (GAN) for green environmental art design. The proposed model combines convolutional feature extraction, semantic embedding, and attention-guided multimodal fusion to jointly represent visual, structural, and ecological information, while an ecological constraint generator and a dual discriminator incorporating structural similarity and energy consumption prediction enable coordinated optimization of aesthetic expression and environmental performance. An adaptive feedback mechanism further updates model parameters according to ecological indicators to achieve closed-loop optimization. Experimental results demonstrate that the generated designs maintain carbon emissions within 48–53 kg CO, energy consumption between 118 and 123 kWh, and ecological consistency indices of 0.87–0.91 while preserving stable visual coordination. Beyond sustainable environmental design, the proposed multimodal perception and optimization strategy provides methodological support for intelligent spatial planning in electromagnetic sensing environments, wireless monitoring infrastructures, and smart built systems where heterogeneous information fusion and energy-aware decision-making are essential for reliable operation.