Abstract: Recommendation has been widely used in business scenarios to provide users with personalized and accurate item lists by efficiently analyzing complex user-item interactions. However, existing recommendation methods have significant shortcomings in capturing the dynamic preference changes of users and discovering their true potential intents. To address these problems, a novel framework named Intent-Aware Graph-Level Embedding Learning (IaGEL) is proposed for recommendation. In this framework, the potential user interest is explored by capturing the co-occurrence of items in different periods, and then user interest is further improved based on an adaptive aggregation algorithm, forming generic intents and specific intents. In addition, for better representing the intents, graph-level embedding learning is designed based on the mutual information comparison among positive intents and negative intents. Finally, an intent-based recommendation strategy is designed to further mine the dynamic changes in user preferences. Experiments on three public and industrial datasets demonstrate the effectiveness of the proposed IaGEL in the task of recommendation.