Abstract: The application of deep learning in food safety detection and risk early warning is becoming more and more extensive, thus providing new opportunities for improving food safety, quality control and authenticity identification. This paper first introduces the basic concept of deep learning and its current development in the field of food safety, and discusses the application of convolutional neural network (CNN), recursive neural network (RNN), transformer architecture and graph neural network (GNN) in food safety detection and risk prediction. Although deep learning performs well in improving the efficiency and accuracy of food safety detection, its practical application still faces challenges such as poor data quality, weak privacy protection capacity and lack of model interpretability. Next, this paper analyzes potential risks that could be brought about by these problems and proposes possible solutions such as promoting data standardization, strengthening privacy protection, and promoting the formulation of policies regarding artificial intelligence. In the future, the combination of deep learning with the Internet of Things (IoT) and blockchain technology and further development of generative artificial intelligence will promote the digital transformation of the food industry and enable the whole-process traceability of food safety monitoring.

Key words: food safety, deep learning, food detection, risk early warning