Abstract: The Long Short-Term Memory (LSTM) related prediction algorithm based on the deep learning technology is widely used in the space weather prediction in recent years. Due to its shortcoming of stacked prediction errors against time, the LSTM-related prediction algorithms can only be used for limited short-term predictions. To improve its performance in the model, we add the solar wind parameters, the sunspot number, the Ap index representing the geomagnetic activity level, and the Dst index corresponding to the magnetic storm ring current as the predictors, to establish a hybrid model based on the LSTM and the DNN (deep neural networks) for medium and short-term forecasting of the global ionosphere TEC map, which can significantly inhibit the increasing trend of the forecast error over time. Test results show that compared with the LSTM model alone, the LSTM-DNN hybrid model enjoys a similar accuracy for the 24-hour prediction of the ionosphere. The average relative accuracy of the 48-hour ionosphere forecast is increased from 79.30% to 81.18%, and that for the 144-hour ionospheric prediction is increased from 64.97% to 77.64%.