Risk Analysis Based on Multi-Source Data and Artificial Intelligence: A Case Study of Pre-Made Dishes

Pre-made dishes have drawn growing attention because of their convenience and rapid market expansion. Their food safety risks, however, are shaped not only by products themselves, but also by the gap between public perception, reported incidents, and inspection records. This study develops a three-stage analytical approach by combining Weibo public opinion data, news media reports, and food inspection records from Gansu Province. First, ERNIE and BERTopic are used to identify public sentiment and discussion topics. The results show that negative sentiment slightly exceeds positive sentiment, with school meals, additives, and food safety as the main concerns. Second, 11,110 pre-made dish-related food safety reports from Food Partner Network are clustered and assessed for incident severity. The results point to drug residues in aquatic products, microbial contamination in egg products, authenticity disputes over meat ingredients, and quality issues in frozen composite foods. Third, based on the 2024 official definition, 12,121 inspection records are screened, and 2783 definition-constrained pre-made dish-associated products are retained. Six imbalanced classification models are then constructed. The Weight + RF model performs relatively well for starch and starch products, with a Precision of 0.7857, an AUC-ROC of 0.7778, and an MCC of 0.4429. The study provides a reference for risk identification and inspection resource optimization under limited pre-made dish inspection data.