Data Mining-Based Food Safety Data Analysis and Application

食品安全事关国家大计。近年来，相关法律法规不断出台，监管部门也在持续完善监管机制。然而，食品安全依然是一个需要高度重视和严格把控的重要问题。本研究基于机器学习方法，构建了多种分类模型，包括支持向量机(SVM)、随机森林(RF)、LightGBM和XGBoost，并对其性能进行了系统比较。结果表明，随机森林模型表现最佳，其次为支持向量机和XGBoost，而LightGBM的性能相对较差。为了进一步提升预测能力，研究进一步构建了Stacking融合模型，将随机森林、XGBoost和支持向量机的预测结果进行集成，并采用随机森林作为元学习器。实验结果显示，Stacking模型显著提高了整体预测性能。本研究结果为食品安全监管提供了重要参考，验证了数据挖掘技术在食品安全领域的有效性。相关技术可辅助监管部门实现食品安全问题的及时预测与处理，从而提升监管工作的效率与准确性。Food safety is a matter of national importance. In recent years, relevant laws and regulations have been continuously introduced, and regulatory authorities have been constantly improving the regulatory mechanisms. However, food safety remains a critical issue that requires high attention and strict control. This study, based on machine learning methods, constructed various classification models, including Support Vector Machine (SVM), Random Forest (RF), LightGBM, and XGBoost, and systematically compared their performance. The results showed that the Random Forest model performed the best, followed by Support Vector Machine and XGBoost, while LightGBM had relatively poorer performance. To further enhance the predictive capability, the study constructed a Stacking ensemble model, integrating the prediction results of Random Forest, XGBoost, and Support Vector Machine, with Random Forest serving as the meta-learner. Experimental results indicated that the Stacking model significantly improved overall predictive performance. The findings of this study provide important references for food safety regulation and validate the effectiveness of data mining techniques in the field of food safety. The related technologies can assist regulatory authorities in timely predicting and handling food safety issues, thereby improving the efficiency and accuracy of regulatory work.