Research on the Fusion Technology of Fashion Design and Neural Networks Based on Fashion Trends

Traditional fashion design relies on the designer's experience, and there are limitations in fashion trend analysis and user demand response. Combining the characteristics of fashion design with Deep Learning, this paper constructs a multi-module intelligent model integrating trend prediction, key point detection, and design generation. The results show that Convolutional Neural Networks are superior to other models in the task of trend prediction on two fashion image data sets. Back Propagation Neural Networks combined with Principal Component Analysis dimension reduction significantly improves the training efficiency and model generalization ability. The model proposed in this study improves the scientificity of fashion trend analysis and design decision-making. It also provides technical support for a personalized clothing customization system based on communication networks and intelligent terminals, which embodies the deep integration of Artificial Intelligence, fashion design, and information technology and has a good interdisciplinary application prospect.