A Machine Learning Framework for Meat Safety Risk Assessment with Cross-Domain Data Fusion

The escalating complexity of global meat supply chains and rising consumption levels heighten public health risks, particularly in high-volume regions. To address this, we developed a novel risk assessment framework integrating multi-source heterogeneous data from seven key origins. The framework implements LLM-enhanced data governance, a Delphi-AHP dual-dimensional indicator system, and an optimized Random Forest model, achieving predictive accuracy between 88.25% and 99.24%. Analysis identified pork as the highest-risk meat category, fresh meat as the riskiest processing type, and a shift since 2022 toward biological hazards, especially pathogens, as the dominant risk over chemical contaminants. This data-driven tool enables proactive, targeted risk management and supports regulatory decision-making for enhanced meat safety.