It is a bottleneck of Apriori algorithm for mining association rules that the candidate set C ₂ is used to generate the frequent 2-item set L ₂. In the Direct Hashing and Pruning (DHP) algorithm, a generated Hash table H ₂ is used to delete the unused candidate item sets in C ₂ for improving the efficiency of generating L ₂. However,the traditional DHP is a serial algorithm, which cannot effectively deal with large scale data. In order to solve the problem, a DHP parallel algorithm, termed H_DHP algorithm, was proposed. First, the feasibility of parallel strategy in DHP was analyzed and proved theoretically. Then, the generation method for the Hash table H ₂ and frequent item sets L ₁, L ₃- L _k was developed in parallel based on Hadoop, and the association rules were generated by Hbase database. The simulation experimental results show that, compared with the DHP algorithm, the H_DHP algorithm has better performance in the processing efficiency of data, the size of the data set, the speedup and scalability.