摘要： 随着城市化与工业化的快速推进，空气污染问题日益严峻，精准预测空气质量指数（AQI）对公众健康与环境保护具有重要意义。然而，现有基于时空图神经网络的空气质量预测方法仍存在明显局限：一方面，受模型结构限制，难以有效建模其他站点在长期历史中通过复杂时空传播路径对目标站点形成的影响；另一方面，现有动态图学习方法主要依赖短序列，无法从长期观测数据中挖掘更具代表性的空间关联模式。为此，提出一种时空上下文感知图网络模型（ST-CAGN）。设计了基于预训练编码器的长序列时空上下文提取模块，将长序列历史数据编码为富含语义信息的低维表示，并高效捕捉跨站点的长期时空依赖；同时，提出一种基于长序列的多尺度动态图学习机制，克服仅利用短期序列构建动态图的局限性。该机制通过从长期历史序列的低维表示中提取稳态空间依赖特征，并与近期波动中捕捉的瞬时空间关联进行自适应融合，从而更精确地刻画站点间复杂的动态空间依赖关系。实验结果表明，ST-CAGN在三个真实空气质量数据集上均显著优于主流基线模型，在6小时、12小时和24小时预测任务中，MAE分别平均下降4.19%、5.47%和6.53%，RMSE平均降低2.10%、3.14%和3.95%，验证了该模型在长序列时空预测任务中的有效性与优越性。

Abstract: With the rapid advancement of urbanization and industrialization, air pollution has become an increasingly severe issue. Accurate prediction of the Air Quality Index (AQI) is of great significance for public health and environmental protection. However, existing spatiotemporal graph neural network-based methods for air quality prediction still exhibit notable limitations. On one hand, due to structural constraints, these models struggle to effectively capture the influence of other stations on target stations through complex spatiotemporal propagation paths over long-term historical data. On the other hand, current dynamic graph learning approaches primarily rely on short-term sequences, failing to extract more representative spatial dependency patterns from long-term observational data. To address these issues, this paper proposes a Spatiotemporal Context-Aware Graph Network (ST-CAGN). The model incorporates a long-sequence spatiotemporal context extraction module based on a pre-trained encoder, which encodes lengthy historical data into low-dimensional representations rich in semantic information and efficiently captures long-range spatiotemporal dependencies across stations. Additionally, a multi-scale dynamic graph learning mechanism based on long sequences is introduced to overcome the limitations of constructing dynamic graphs solely from short-term sequences. This mechanism extracts steady-state spatial dependency features from low-dimensional representations of long-term historical sequences and adaptively integrates them with transient spatial correlations captured from recent fluctuations, thereby more accurately modeling the complex dynamic spatial dependencies between stations. Experimental results demonstrate that ST-CAGN significantly outperforms mainstream baseline models on three real-world air quality datasets. For 6-hour, 12-hour, and 24-hour prediction tasks, the MAE decreased by an average of 4.19%, 5.47%, and 6.53%, respectively, while the RMSE was reduced by an average of 2.10%, 3.14%, and 3.95%, validating the effectiveness and superiority of the proposed model in long-sequence spatiotemporal forecasting tasks.