Abstract: Recent advancements in the field of sequential recommender have focused on refining user interests through various methods, such as clustering historical interactions or utilizing graph convolutional neural networks to capture multi-level correlations among interactions. However, while these approaches have significantly advanced the field, they often overlook the interactions between users with similar behavioral patterns and the impact of irregular time intervals within interaction sequences on user interests. Based on the above problems, a multi-interest aware sequential recommender model (MIRec) based on contrastive learning is proposed. This model takes into account both local preference information, including item dependence and location dependence within a sequence, and global preference information obtained through a graph information aggregation mechanism among similar users. The user representations, which incorporate both local and global preferences, are fed into a capsule network to learn multi-interest representations within the user interaction sequence. Subsequently, the user’s historical interaction sequences are brought closer to enhanced interaction sequences through contrastive learning. This process results in the generation of a user’s multi-interest representation that is insensitive to time intervals, ultimately leading to more accurate recommendations for users. The effectiveness of this model is verified on two real datasets, and the experimental results verify the effectiveness of the model.