The invention discloses an improved association rule report data mining method based on mutual exclusion expression, relates to a knowledge discovery and data mining method in the field of data science, and solves the problems of high memory consumption and low efficiency when a traditional association rule algorithm processes massive data. The method comprises the steps of 1, converting data intotransaction data based on a data threshold range, and obtaining a binary sparse matrix with grouping labels based on data logic; 2, obtaining a set in which all frequent items are 1, and removing a non-frequent item set to obtain a new grouping result; and 3, performing self-connection iterative search on the frequent item set to search the frequent item set, and cutting and iterating the candidate item set until a new frequent item set cannot be generated, thereby obtaining an association rule mining result. The basic idea of the method is to convert structured data into transaction data, generate groups based on a mutual exclusion relationship, and perform rule mining, thereby reducing the computing memory and improving the computing efficiency. The application scene is wide, and the social and economic values are very high.