Abstract: In the intelligent environment, by analyzing the similarity of the user's daily activity sequences, it is possible to group users with similar behaviors, detect abnormal behaviors of users and query other behavior sequences that are similar to a given sequence, so as to personalize the user finely. That can provide users with a perfect personalized information services. The existing research focuses on the activity similarity calculation without spatiotemporal information or with fixed spatiotemporal information, and does not measure the similarity of user behavior sequences from different levels. In order to realize the dynamic cognition of multi-granularity and multi-view of user behavior, a multi-granularity spatiotemporal sequence algorithm (MGSSA) based on Needleman-Wunsch algorithm was proposed. It was arranged to extend the score function of NW algorithm to combine the temporal information and spatial information, and to realize the similarity of spatiotemporal event sequences from different granularities through granular control. Finally, some experiments were carried out. The results show that the multi-granular spatiotemporal sequence alignment algorithm is effective and feasible.