With manufacturing technology developing persistently, hardware manufacturing cost becomes lower and lower. More and more computers equipped with multiple CPUs and enormous data disk emerge. Existing programming modes make people unable to make effective use of growing computational resources. Hence cloud computing appears. With the utilization of Map Reduce parallelized model,existing computingand storage capabilities are effectively integrated and powerful distributed computingability is provided. Association rules can forcefully get a horizontal relation in the big data,the Apriori algorithm is one of the most significant association rules. Traditional mining based on parallel Apriori algorithms needs much more time in data IO with the increasing size of large transaction database.This paper improves the Apriori algorithm from compressing transactions,reducing the number of scans and simplifying candidate set generation. And then the improved algorithm is parallelized on the Hadoop framework. The experiments show that this improved algorithm is suitable for large-scale data mining and has good scalability and effectiveness.