Abstract: Abstract: Aiming at the problems that the support measure is not a good indicator of the interestingness of patterns and the quality of reported sequential patterns is not evaluated, a statistically significant sequential patterns mining algorithm called ISSPM (Influence-based Significant Sequential Patterns Mining) was proposed. First, all sequential patterns that met the minimum interestingness constraint were mined recursively. Then, the itemsets permuting method was introduced to construct permutation null distributions for all the sequential patterns. Finally, the statistic values of the evaluated sequential patterns were calculated from the permutation null distributions, and all statistically significant sequential patterns were reported. In the experiments with the PSPM (Prefix-projected Sequential Patterns Mining), SPDL (Sequential Patterns Discovering under Leverage) and PSDSP (Permutation Strategies for Discovering Sequential Patterns) algorithms on the real-world data sets, the ISSPM algorithm reports fewer but more interesting sequential patterns. In addition, the experimental results on the synthetic sequential data sets show that the average proportion of the false positive sequential patterns reported by the ISSPM algorithm is 3.39%, and its discovery rate of embedded patterns is not less than 66.7%, which are significantly better than those of the above algorithms. Therefore, the statistically significant sequential patterns reported by the ISSPM algorithm can reflect more valuable information in data sets, and the decisions made based on the information are more reliable.