Deep learning of artificial intelligence and information digitization model

Through the exploration of artificial intelligence’s deep learning capabilities and the process of information digitization, this study reveals the underlying mechanisms of information transformation and optimization. It systematically elaborates on self-cognitive learning in artificial intelligence, dynamic learning processes, and interactive information transformation mechanisms, while constructing a model for information digitization. The results demonstrate that deep learning in artificial intelligence achieves information transformation and optimization through symbolic processing and probabilistic mapping. Digital technologies convert information on physical-world activities into computable digital symbols, and the central operational logic of artificial intelligence in dynamic information organization lies in establishing a dynamic correspondence among data, resource allocation, and economic decision-making. By exploring how generated information shapes future actions, this study uncovers the fundamental principles of the digital creation of productive forces by artificial intelligence. Thus, it not only provides theoretical insights into the new productive forces in the intelligent economy, but also strengthens the theoretical framework of the intelligent digital economy, and proposes strategic recommendations for the development of China’s intelligent economy.