Data mining is one of the hot research topics in the field of databases in recent years. Association analysis is one of the key techniques in data mining, which mainly involves identifying all frequent itemsets in the database, and then generating association rules from the frequent itemsets. This article takes aircraft as the research object, intelligently processes a large amount of experimental data of aircraft control systems, and studies association rule mining methods based on discrete and mixed data, as well as association rule mining methods based on continuous data. For the case of discrete and mixed data, research is conducted based on the Apriori algorithm to obtain corresponding association rules. For continuous data types, particle swarm optimization (PSO) algorithm is used for processing to obtain corresponding association rules. Then, association rules are mined from a large amount of data to abstract design experiences that are conducive to judgment and comparison, improving the efficiency and speed of control system design. Finally, the effectiveness of the algorithm is verified through simulation experiments and analysis.