Abstract:As consumer demand for food quality and safety continues to increase，the limitations of traditional detection technologies in terms of timeliness，throughput，and non-destructiveness have become more apparent，creating a bottleneck in maintaining the safety of the food supply chain.Therefore，there is an urgent need to develop an innovative testing technology that is efficient，accurate，and suitable for large-scale applications.Deep learning，a machine learning technique based on deep neural networks，provided strong support for food authenticity identification.This paper expounded on the basic concepts of deep learning，as well as its research progress and application status in food authenticity identification.The principles of traditional machine learning and deep learning were discussed，along with their applications in food authenticity identification，and the development directions for deep learning technologies in food authenticity identification were also examined.The aim was to provide insights and guidance for future research and technological innovation in the field of food quality and safety detection.