Point-of-interest (POI) recommendations leverage the vast amounts of GPS data collected from location-based social networks to identify frequent patterns and current interests from users’ historical check-in trajectories, enabling accurate predictions of the next POI a user will visit. Graph neural network-based models have made significant breakthroughs in this field by effectively integrating global information. However, current mainstream models tend to focus primarily on POI check-in sequences, neglecting the rich spatiotemporal dynamics inherent in the trajectory data and their inability to dynamically model the heterogeneous importance of spatiotemporal features, which vary across users, locations, and temporal contexts. To address these limitations, we propose a Context-aware Spatiotemporal Graph Attention Network for next-POI recommendations. Our model introduces a novel graph attention mechanism that dynamically adjusts the importance of different spatiotemporal features based on the specific context of user behaviors. This context-awareness enables the model to effectively capture both temporal and spatial homogeneity or heterogeneity in user movement patterns, adapting feature weights according to individual preferences and situational factors. By modeling the varying importance of spatiotemporal features across different contexts, our model achieves more personalized and accurate POI recommendations. Experimental results on real-world datasets demonstrate the effectiveness of our proposed approach in improving the performance of POI recommendation tasks.