<p>Sequential recommendation is an important research task in the field of recommendation systems, where precise modeling of the dynamic evolution of user interests from historical interactions is essential for enhancing performance. To address the limitations of existing methods in capturing the diversity of long-term interests, the dynamics of short-term user interest, and the hierarchical relationship between them, this paper proposes an end-to-end hierarchical long and short-term sequential recommendation model. First, the proposed model leverages a dynamic routing mechanism to adaptively aggregate users’ long-term historical interactions, generating a multi-vector representation of longterm user preference. Simultaneously, a self-attention mechanism is employed to aggregate short-term interaction sequences, effectively capturing short-term user interest. In addition, a hierarchical matching mechanism is designed to align long and short-term user interest, mining the long-term user preference most relevant to the current short-term user interest through similarity-based extraction, and fusing them using time encoding to produce the final user preference representation. Finally, a prediction framework based on attention mechanisms integrates both long-term user preference and short-term interaction information to achieve efficient sequential recommendation. The experimental results indicate that the proposed method achieves significantly better performance than existing state-of-the-art sequential recommendation models across multiple evaluation metrics, validating its effectiveness and superiority.</p>