Because of the rapid growth of data, the high utility sequential pattern mining algorithms' efficiency decreases seriously. In view of this, we propose a high utility sequential pattern mining algorithm based on MapReduce, namely HusMaR. This algorithm is based on MapReduce, which using the utility matrix to generate candidate efficiently, random mapping strategy to balance of computing resources and field-based pruning strategy to prevent an explosion. Experimental results show that in the large scale of data, the algorithm achieves a high parallel efficiency.