With recommender systems broadly deployed in various online platforms, many efforts have been devoted to learning user preferences and building effective sequential recommenders. However, existing work mainly focuses on capturing user implicit preferences from historical interactions and simply matching them with the next behavior, instead of predicting user explicit intentions. This may lead to inappropriate recommendations. In light of this issue, we propose the adversarial user intention learning approach for sequential recommendaiton, named AuriSRec. The major novelty of our approach is to explicitly predict user current intentions when making recommendations, by inferring their decision-making process as explained in target reviews (reviews written after interacting with the ground-truth item). Specifically, AuriSRec conducts adversarial learning between an intention generator and a discriminator. The generator predicts user intentions by taking their historical reviews and behavioral sequences as inputs, while target reviews provide guidance. Beyond typical sequential modeling methods in the field of natural language process (NLP), a decoupling-based review encoder and a hybrid attention fusion mechanism are introduced to filter noise and enhance the generation capacity. On the other hand, the discriminator determines whether the intention is generated or real based on their matching degree to the target item, thereby guiding the generator to produce gradually improved intentions. Extensive experiments on five real-world datasets demonstrate the effectiveness of our approach.