Accurately predicting bike-sharing flow is essential for optimizing the supply-demand balance of shared bikes and enhancing urban residents' travel convenience. To address the issues of low prediction accuracy and insufficient capture of spatiotemporal characteristics in bike-sharing flow prediction, a hybrid Convolutional-Recurrent Neural Network (Conv3D-GRU) model is proposed. Using Chicago's 2022 full-year bike-sharing data, experiments were conducted, and the results were compared with those of the 3D Convolutional Neural Network (3D-CNN) model and the Convolutional Long Short-Term Memory (ConvLSTM) model. The model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). Experimental results show that compared with the 3D-CNN and ConvLSTM models, Conv3D-GRU is improved by 3.25%, 4.90%, 1.14% and 11.94%, 13.70% and 2.46% on RMSE, MAE and R2, respectively. This demonstrates that the Conv3D-GRU model has lower prediction errors and higher prediction accuracy, making it an effective and reliable approach for forecasting bike-sharing inflow and outflow.