Abstract: To address the limitations of traditional session-based recommendation algorithms that rely solely on explicit information while overlooking potential interactions between sessions, this paper proposes a novel integrated modeling approach based on gating mechanisms and graph attention networks—IM-GGN (Integrated Modeling Gated Graph Network). This model simultaneously captures structured relationships between items and unstructured associations across sessions to enhance recommendation performance. Specifically, the model comprises two main components: the Structured Pattern Learning (SPL) module and the Unstructured Pattern Learning (UPL) module. The SPL module integrates graph neural networks with gating mechanisms to model sequential dependencies and long-range relationships within sessions. Meanwhile, the UPL module leverages graph attention mechanisms to capture unstructured inter-session correlations, thereby extracting contextual user preferences. Experimental results on multiple public datasets demonstrate that the proposed method achieves notable performance improvements, confirming its effectiveness in session-based recommendation tasks.