Frequent itemset mining is a basic problem of data mining and plays an important role in many data mining applications.In order to solve the problems of the parallel frequent itemset mining algorithm(MrPrePost) in big data environment,such as algorithm efficiency degradation,unbalanced load of computing nodes and redundant search,this paper proposes a parallel frequent itemset mining algorithm(PFIMND),which is based on N-lists and DiffNodeset.Firstly,according to the advantages of N-list and DiffNodeset data structures,the data set sparsity estimation function(SE) is designed,and one of them is selected to store data according to the data set sparsity.Secondly,the computational estimation function(CE) is proposed to estimate the load of each item in the frequent 1-item set F-list,and the load is evenly grouped according to the computational cost.Finally,the set enumeration tree is used as the search space.In order to avoid combination explosion and redundant search problems,the superset pruning strategy and the pruning strategy based on width first searches are designed to generate the final mining results.Experimental results show that compared with the similar algorithm(HP-FIMND),the effect of PFIMND algorithm in mining frequent itemsets on Susy dataset is improved by 12.3%.