Abstract: Total electron content (TEC) is the most important ionospheric parameter since the satellite era, and has important theoretical significance and application value. This paper proposes a global ionospheric TEC prediction model based on deep learning methods. We use an encoder-decoder structure to match the convolution-optimized long and short-term memory network (ConvLSTM) to achieve global TEC spatial and temporal prediction. The spatial latitude and longitude resolution of this model is 5°×2.5°, and the time accuracy is one hour. The prediction results when the geomagnetic activity is calm indicate that the model advances the global root mean-square error (RMSE) predicted for one day is less than 1.5 TECU, and the predicted root mean square error within one week in advance is less than 2 TECU. During the period of weak magnetic storm, the prediction error of this model is about 2.5 TECU. By comparing the results of different geomagnetic activity indexes and different latitudes, we found that with the increase of the forecast time and the intensity of geomagnetic activity, the error of this model will gradually increase, and the model has better predictions in the middle and high latitudes.