Rare sequential pattern mining aims to discover infrequent and important patterns in sequence databases. However， current sequential pattern mining methods mostly determine whether a pattern occurs in a sequence or not， ignoring the repetition of the pattern in the sequence， that is， the user’s level of interest， resulting in bias in the mining results. To tackle this issue， a rare sequential pattern mining method with adaptive gap under one-off condition was proposed， namely ORP （One-off Rare sequential Pattern mining）. In the method， the number of repetitions of the pattern in the sequence was calculated using one-off condition， and the sequence features were reflected using adaptive gaps. To avoid the inefficient sequential traversal of the original database in support calculation process required by the traditional algorithms， an inverted index structure was established， which stores each transaction and its location information occurred in the original database， thereby eliminating the need for any redundant traversal of the database and improving efficiency of the support calculation. Besides， in the process of candidate pattern generation， a pattern connection strategy was used to generate candidate patterns. To further reduce the number of candidate patterns， a pruning strategy was proposed， thereby improving the mining speed. Ablation experimental results on five real datasets show that the running time of the proposed method is significantly shorter， thus verifying the superiority of the proposed method.