Information spread within social networks is a complex process with broad implications. Predicting information diffusion is crucial for understanding information spread within social networks. However, previous research has primarily focused on the homogeneity characteristics of internal cascades, such as temporal and social relationships, neglecting the impact of external information propagation. Additionally, conventional methods of feature integration simply merge cascade and user embeddings, which may introduce excessive redundant information and result in the loss of valuable contextual information critical for accurate predictions. To address these limitations, we present a novel model, the Equivariant Diffusion-based Sequential Hypergraph Neural Network with Co-Attention Fusion (EDSHNN-CAF). Within its cascade feature learning module, the model proposes hypergraphs with equivariant diffusion operators to incorporate external cascade influences alongside internal features. This approach effectively captures complex high-order interconnections and accurately reflects the dynamics of information diffusion. In the feature fusion and prediction module, a co-attention mechanism is designed to seamlessly integrate cascade and user embeddings, revealing their complex interdependencies and significantly enhancing predictive capabilities. Experimental results on four real datasets showcase the promising performance of EDSHNN-CAF in predicting information diffusion, outperforming existing state-of-the-art information diffusion prediction models.