Aiming at the problem of inefficient data mining of association rules in a big data environment，the Eclat algorithm is used to use a vertical database to convert the merging of transactions into collective operations. We researched a big data parallel association rule mining algorithm-Sp-IEclat ( Improved Eclat algorithm on Spark Framework) . The algorithm is based on the Spark framework of memory computing，reduces disk input and output， reduces I /O load，and uses bitmap operations to reduce the time of intersection and CPU usage. The pruning technique of prefix division is used to reduce the amount of data in the intersection operation to reduce the operation time. The mushroom dataset and the webdocs dataset are used to test under two big data platforms. The experimental results show that the time efficiency of the Sp-IEclat algorithm is better than the Eclat algorithm under the MapＲeduce framework and the FP-Growth algorithm and the Eclat algorithm under the Spark framework. The value obtained from the performance monitoring of the cluster shows that，compared with the FP-Growth algorithm and the Eclat algorithm under the Spark framework，the CPU usage and I /O cluster load of Sp-IEclat are smaller.