Food safety issues have grown in recent years, attracting great research attention. In addition to increasing supervision, regulatory authorities and related companies are currently trying to use the existing regulatory system to develop real-time data monitoring systems. In this study, food safety inspection data were collected from the information release platform of management departments across China. These data were processed with chemical hazardous substances as the objects of concern and were then classified according to hazardous substances, record product name, inspection location, and other categories. The current risk of chemical hazards in food was analyzed, and key information from the inspection data was mined. Frequent items and association rules of the inspection data were generated by the Apriori algorithm and evaluated according to the support, confidence, and rule interestingness (RI) to obtain key information to aid in developing an improved food safety inspection system. The results show that data mining methods can be used to obtain early warning information from food safety inspection data and are more efficient than traditional statistical methods. With data mining methods, an efficient early warning system can be established to assist management departments and manufacturers in ensuring food safety and quality.

近年来，食品安全问题日益突出，引起了广泛的研究关注。除了加大监管力度，监管部门和相关企业目前也在尝试利用现有的监管体系，开发实时数据监控系统。本研究从全国管理部门信息发布平台收集食品安全检测数据。这些数据以化学有害物质为关注对象进行处理，然后按照有害物质、备案产品名称、检验地点等类别进行分类。对当前食品中化学危害的风险进行分析，并从检验数据中挖掘关键信息。通过 Apriori 算法生成检验数据的频繁项和关联规则，并根据支持度、置信度和规则兴趣度 (RI) 进行评估，以获得关键信息，以帮助开发改进的食品安全检验系统。结果表明，数据挖掘方法可用于从食品安全检测数据中获取预警信息，比传统的统计方法效率更高。通过数据挖掘的方法，可以建立高效的预警系统，协助管理部门和生产企业确保食品安全和质量。结果表明，数据挖掘方法可用于从食品安全检测数据中获取预警信息，比传统的统计方法效率更高。通过数据挖掘的方法，可以建立高效的预警系统，协助管理部门和生产企业确保食品安全和质量。结果表明，数据挖掘方法可用于从食品安全检测数据中获取预警信息，比传统的统计方法效率更高。通过数据挖掘的方法，可以建立高效的预警系统，协助管理部门和生产企业确保食品安全和质量。