Spatiotemporal forecasting finds extensive applications in domains such as pollution management, transportation, energy, and meteorology. Predicting PM_2.5 concentration, as a quintessential spatiotemporal forecasting task, necessitates the analysis and utilization of spatiotemporal dependencies within air quality data. Existing studies on spatiotemporal graph neural networks (ST-GNNs) either employ predefined heuristic rules or trainable parameters for adjacency matrices, posing challenges in accurately representing authentic inter-station relationships. This study introduces the adaptive hierarchical graph convolutional neural network (AHGCNN) to address these issues concerning PM_2.5 prediction. Firstly, a hierarchical mapping graph convolutional architecture is introduced, employing distinct self-learning adjacency matrices at different hierarchical levels, efficiently uncovering unique spatiotemporal dependencies among various monitoring stations. Secondly, an attention-based aggregation mechanism is employed to connect adjacency matrices across different hierarchical levels, expediting the convergence process. Finally, the hidden spatial states are fused with gated recurrent unit (GRU), forming a unified predictive framework capable of concurrently capturing multi-level spatial and temporal dependencies, ultimately delivering the prediction results. In the experiments, the proposed model is comparatively analyzed with seven mainstream models. The results indicate that the model can effectively capture the spatiotemporal dependencies between air monitoring stations, improving predictive accuracy.