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In this paper, we propose a novel Total Electron Content (TEC) map prediction model, named ED‐AttConvLSTM, using a Convolutional Long Short‐Term Memory (ConvLSTM) network and attention mechanism based on encoder‐decoder structure. The inclusion of the attention mechanism enhances the efficient utilization of spatiotemporal features extracted by the ConvLSTM, emphasizing the significance of crucial spatiotemporal features in the prediction process and, as a result, leading to an enhancement in predictive performance. We conducted experiments in East Asia (10°N–45°N, 90°E−130°E). The ED‐AttConvLSTM was trained and evaluated using the International GNSS Service TEC maps over a period of six years from 2013 to 2015 (high solar activity years) and 2017 to 2019 (low solar activity years). We compared our ED‐AttConvLSTM with IRI‐2016, COPG, LSTM, GRU, ED‐ConvLSTM and ED‐ConvGRU. The results indicate that our model surpasses the comparison models in forecasting both high and low solar activity years, across most months and UT moments in a day. Moreover, our model exhibits notably superior prediction performance during the most severe phases of a magnetic storm when compared to the comparison models. Subsequently, we then also discuss how the prediction performance of our model is affected by latitude. Finally, we discuss the diminishing performance of our model in multi‐day predictions, demonstrating that its reliability for forecasts ranging from one to 4 days in advance. Beyond the fifth day, there is a pronounced decline in the model's performance. Plain Language Summary: High precision prediction of Total Electron Content (TEC) is of great significance for improving the accuracy of global satellite navigation systems. In this paper, we introduced the attention mechanism into the ionospheric TEC map prediction model to adaptively weight the ionospheric TEC spatiotemporal features, highlighting the contribution of important spatiotemporal features in TEC map prediction. Results showed that the prediction performance of our model is improved compared with the other six models. Key Points: Introducing ED‐AttConvLSTM, a novel Total Electron Content (TEC) map prediction model utilizing Convolutional Long Short‐Term Memory and an attention mechanism in an encoder‐decoder frameworkThe incorporation of attention mechanisms markedly decreased root mean square error in TEC map predictions for 2015 and 2019Our model excels over 6 state‐of‐the‐art models, affirming its robustness and reliability for TEC map prediction [ABSTRACT FROM AUTHOR]

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model. [ABSTRACT FROM AUTHOR]