Research on the Application of Intelligent Systems in the Collection of Digital Graphics of Intangible Cultural Heritage with AI Technology

This study focuses on the field of artificial intelligence (AI) and explores the application of intelligent systems in the compilation of non-material cultural heritage (NMC) pattern intelligence. Aiming to improve the efficiency and accuracy of drawing data aggregation, the paper has innovated an intelligent architecture based on generative adversarial network (GAN) to achieve automatic and accurate extraction of NMC graphic features. The first step is to tailor the deep learning program for NMC patterns and confirm its feasibility through simulation models. The system is optimized with the help of massive NMC image data, and the fluctuation of algorithm configuration on the collection effect is fully considered. Empirical data show that this intelligent platform has an accuracy of up to 97.5% in the NMC pattern capture operation, and greatly reduces the frequency of human intervention. More strikingly, in the face of the challenge of the diversity of NMC items, the system shows excellent adaptability and is suitable for the collection of a wide range of NMC graphics. Through detailed statistical analysis, the paper confirms the practical value and robust performance of the system.