When using the way of pattern growth to construct tree structure, the exiting algorithms for mining probabilistic frequent itemsets suffer many problems, such as generating large number of tree nodes, occupying large memory space and having low efficiency. In order to solve these problems, a Progressive Uncertain Frequent Pattern Growth algorithm named PUFP-Growth was proposed. By the way of reading data in the uncertain database tuple by tuple, the proposed algorithm constructed tree structure as compact as Frequent Pattern Tree (FP-Tree) and updated dynamic array of expected value whose header table saved the same itemsets. When all transactions were inserted into the Progressive Uncertain Frequent Pattern tree (PUFP-Tree), all the probabilistic frequent itemsets could be mined by traversing the dynamic array. The experimental results and theoretical analysis show that PUFP-Growth algorithm can find the probabilistic frequent itemsets effectively. Compared with the Uncertain Frequent pattern Growth (UF-Growth) algorithm and Compressed Uncertain Frequent-Pattern Mine (CUFP-Mine) algorithm, the proposed PUFP-Growth algorithm can improve mining efficiency of probabilistic frequent itemsets on uncertain dataset and reduce memory usage to a certain degree.