Sequence recommendation systems usually learn users’ personalized preferences based on historical behavior sequences of users. Previous methods often use rich item interaction informations combined with context to mine user sequential patterns. However, the time decay and dynamic evolution of users’ preferences are rarely taken into consideration. In this paper, we propose TDH4Rec(Time-aware Dual-kernel Hawkes process for sequential recommendation). First, we establish a time-aware attention embedding module. We utilize time sensitivity and importance of interactions to capture the time dependence and frequency dependence of items during the interaction process. Secondly, we design a Hawkes process based dual-kernel learning module. The Hawkes interaction kernel function with historical interaction effect and the Hawkes time kernel function with time decay effect are designed to capture the influence of historical interaction and the influence of time change of interacted items, respectively. Finally, extensive experiments on three real-world datasets show the efficacy of our model compared with conventional methods and state-of-the-art methods.