As information technology develops, recommendation system serves as an important tool in the era of information overload and plays an increasingly important role. Traditional recommendation systems based on content and collaborative filtering tend to model the interaction between users and items in a static way to obtain users’ previous long-term preferences. Because users’ preferences are often dynamic, unsustainable, and behavior-dependent, sequential recommendation methods model the interaction histories between users and items as ordered sequences, which can effectively capture the dependencies between items and users’ short-term preferences. However, most sequential recommendation models overemphasize the behavior order of user-item interaction and ignore the temporal information in interaction sequences. In other words, they implicitly assume that adjacent items in the sequences have the same time interval, which leads to limitations in capturing users’ preferences that include temporal dynamics. In response to the above problems, this study proposes a self-attention-based network for time-aware sequential recommendation (SNTSR) model, which integrates temporal information into an improved self-attention network to explore the impact of dynamic time on the prediction of the next item. At the same time, SNTSR independently calculates position correlation to eliminate the noise correlations that may be introduced and enhance the ability to capture users’ sequential patterns. Extensive experimental studies are carried out on two real-world datasets, and results show that SNTSR consistently outperforms a set of state-of-the-art sequential recommendation models.