Next Point-of-Interest(POI) recommendation focuses on predicting the next location a user would visit, where the demands of users depend on the implicit user preference implied by the past behavioral trajectories and are influenced by future behavior factors as well. However, most existing methods that consider user future preference merely focus on the fine-grained POI check-in trajectories of single users’ sequential migrations. This overlooks the potential benefits of incorporating coarse-grained signals (global check-ins) to enhance user preference learning. And yet, traditional global coarse-grained structures are mainly constructed by graph neural networks and fixed graph structures to obtain POI representations that aggregate information and capture node correlations, which can move away from the optimal graph structure due to noise and adaptation issues, leading to sub-optimal performance of recommendations. In this paper, we propose a framework based on Adaptive Graphs Learning and Future Preferences(AGFP), which develop an adaptive POI graph matrix and learns it via similarity learning with POI embeddings, after which we merge the learning POI representation with the side information and spatio-temporal information, then generate the user’s future preferences through the fusion of long-term preference encoder with the short-term preference encoder in a self-ensembling manner. Our model is extensively experimented on three real-world datasets. The experimental results demonstrate that AGFP shows superior performance.