Design Optimization of Cultural Creative Products Based on Neural Network Algorithm

The design optimization of cultural creative products is crucial for enhancing aesthetic appeal and functionality while preserving cultural significance. This study addresses the challenge of efficiently optimizing designs by leveraging neural network algorithms to analyze and generate innovative product variations. The problem lies in the subjective nature of cultural design, which traditional methods struggle to quantify. A neural network-based approach is employed to learn design patterns from historical and contemporary cultural artifacts, improving optimization accuracy. Alternative algorithms like Generative Adversarial Networks (GANs) and Transformer models are suggested for enhanced creativity and efficiency. The research utilizes the Wiki-Art: Visual Art Encyclopaedia comprising diverse cultural product designs with annotated features. Novelty emerges from the system's ability to achieve higher accuracy (98.5%) and faster convergence compared to conventional methods, validated through performance metrics like Mean Squared Error (MSE) and user satisfaction scores. The proposed framework demonstrates superior adaptability in blending traditional and modern design elements, offering a scalable solution for the cultural creative industry.