Accurate tide level prediction is crucial for storm surge prevention and mitigation. In this study, we propose a regional multi-station tide-level prediction model (RMSTL) based on a graph convolution recurrent network that utilizes spatial and temporal information from historical tide levels and meteorological data to predict tide levels at multiple stations. Six stations in Changjiang Estuary, China, were selected for this case study. We explored the change in the accuracy of forecasting the tide-level during typhoons under a distinct topology of the tidal station network and the weights of the stations. The results showed that: (1) the prediction accuracy of RMSTL model behaved better than that of the other three widely used models; (2) the RMSTL model can be made more accurate by adding the weights of the edges or the number of edges of the topology; the value of RMSE decreased from 0.369 to 0.278, and the value of MAE decreased from 0.260 to 0.189; (3) when the dataset was limited, increasing the weights of the edges had a slightly better effect than increasing the weights of the edges in terms of improving the accuracy of the prediction results. The proposed method opens new avenues for forecasting storm surges.