With the increasing globalization of supply chains, ensuring food safety has become more complex, necessitating advanced approaches for risk assessment. This study aims to review the transformative role of machine learning (ML) and deep learning (DL) in enabling intelligent food safety management by efficiently analyzing high-quality and nonlinear data. We systematically summarize recent advances in the application of ML and DL, focusing on key areas such as biotoxin detection, heavy metal contamination, analysis of pesticide and veterinary drug residues, and microbial risk prediction. While traditional algorithms including support vector machines and random forests demonstrate strong performance in classification and risk evaluation, unsupervised methods such as K-means and hierarchical cluster analysis facilitate pattern recognition in unlabeled datasets. Furthermore, novel DL architectures, such as convolutional neural networks, recurrent neural networks, and transformers, enable automated feature extraction and multimodal data integration, substantially improving detection accuracy and efficiency. In conclusion, we recommend future work to emphasize model interpretability, multi-modal data fusion, and integration into HACCP systems, thereby supporting intelligent, interpretable, and real-time food safety management.

With the advent of the information age, food safety issues often trigger public panic and even disrupt social order. Traditional public opinion analysis methods struggle to handle massive amounts of data and effectively predict public sentiment trends. To address this problem, this paper proposes a deep reinforcement learning-based method for predicting public opinion trends related to food safety. First, a multimodal event extraction method is designed to extract image information and fuse it with text to generate new textual information. Then, an event detection model is designed based on the HDBSCAN clustering algorithm to more accurately identify emerging issues in safety-sensitive areas. Finally, this paper proposes a deep reinforcement learning-based model to predict public sentiment trends regarding food safety. Experimental results show that, compared with traditional methods, the proposed method has higher accuracy and adaptability in handling sudden or delayed public opinion events.