<p>The rapid expansion of online social networks has led to explosive growth of information cascades, necessitating effective prediction methods for both research and industry. While deep learning approaches prevail in this domain, existing methods face several critical challenges in capturing the dynamic nature of cascades and integrating temporal and structural information effectively. This paper presents CasSubTS, a novel subgraph-based information cascade prediction model to address these limitations. CasSubTS samples subgraphs from the cascade graph at different time steps to capture dynamic node changes. It enhances node representation by integrating in-degree and out-degree as structural features and uses a multi-head graph attention network to learn multi-scale structural information. The model employs an attention mechanism to aggregate important temporal information of nodes, providing richer temporal data for the subsequent Bi-GRU model. Additionally, channel attention is used to prioritize the fusion of spatiotemporal features, improving the focus on key features and reducing noise interference. Integrated features are fed into a multilayer perceptron for incremental prediction. Extensive experiments on two real-world datasets and a synthetic dataset compared with six other classic and recent prediction models demonstrate the superior effectiveness of CasSubTS in accurately predicting information cascades.</p>