At present, China’s food sampling inspection work has the problems of large workload and high cost. In this paper, we try to extract some valuable association rules from the national sampling data by analyzing them. Based on the data of spices major category in the 2019 national sampling database, this paper applies CARMA algorithm to mine association rules for the data studied in terms of food categories (sub-categories), sampling provinces, testing sites, contaminant categories, and relative risk levels of contaminants, and eight relatively valuable and effective strong association rules are obtained after the experiment, and some of them are interpreted. The results show that through association rule mining of relevant sampling data, the connection existing between some key and non-key testing objects and some unqualified items can be determined, which in turn can provide some reference for the allocation of resources for sampling work.