Air quality predictions play a critical role in shaping governmental policies and measures worldwide, aimed at mitigating the health risks posed by air pollution to the public. However, current spatiotemporal prediction methods frequently encounter challenges in accurately extracting spatial features, which adversely affects the overall predictive performance. To mitigate this limitation, we introduce AGTCN, a novel air quality forecasting model that integrates an adaptive gating mechanism. The model leverages an enhanced Graph Convolutional Network (GCN), wherein the adaptive gates dynamically regulate spatial feature extraction based on geographic inputs. Subsequently, the refined spatial representations are fused with a Temporal Convolutional Network (TCN) to model the joint spatiotemporal dynamics. Ultimately, the synthesized features are passed through a prediction layer to generate the final outputs. We validated the model’s performance using a real dataset, and both comparative experiments and ablation studies demonstrated the model’s effectiveness.