Research on GAN-based Product Appearance Design Trend Prediction and Intelligent Generation Algorithms

This paper investigates the prediction of product appearance design trends and the development of intelligent generative algorithms based on Generative Adversarial Networks (GANs). By integrating theoretical analysis with practical case studies, the study systematically explores the principles, operational mechanisms, and advantages of applying GANs in product appearance design. An enhanced intelligent generative algorithm is proposed, which incorporates adaptive feature learning and trend-aware generation strategies to improve both the novelty and relevance of the generated designs. The algorithm's performance is rigorously validated through comparative data analysis, demonstrating significant improvements in design efficiency, trend prediction accuracy, and creative diversity. The findings indicate that GAN-based approaches can provide reliable scientific guidance for forecasting design trends, while also offering practical solutions for accelerating the product development cycle. This research contributes to advancing the theoretical understanding of AI-driven design and offers actionable methodologies for designers seeking to integrate intelligent generative tools into contemporary product development practices.