A prediction method based on the multi-channel graph aggregated attention mechanism was proposed, to address the challenges of limited spatial scope, insufficient spatiotemporal information capture, and low accuracy in short-term bike-sharing demand prediction. Firstly, the city was divided into multiple bike-sharing virtual stations using a flow-adjusted virtual station partitioning method according to bike flows in different areas. A dynamic adjacency matrix was constructed using the origin-destination (OD) matrix between stations to form a bike-sharing graph network structure. Next, spatial information of stations across different time periods was captured via a multi-channel graph aggregation module, which was combined with a multi-head self-attention module to capture temporal correlations. Finally, a cross-attention mechanism, along with exogenous variables, was introduced to uncover potential relationships among various variables. Experiments conducted in Shenzhen and New York demonstrated that the model significantly outperformed other deep learning methods across various time periods and regions, maintaining stable and low prediction errors. The results confirmed that the dynamic adjacency matrix and the cross-attention mechanism integrating external features could effectively enhance the prediction accuracy of shared bike usage.