Abstract: Objectives:  Accurate prediction of ionospheric total electron content (TEC) is of great research significance to improve the accuracy of GNSS navigation and positioning.   Methods:  We propose an ionospheric TEC prediction model based on particle swarm optimization (PSO) algorithm and Elman neural network (ENN). Firstly, high accurate regional ionospheric map (RIM) in China is set up by using the GNSS observation data from the Crustal Movement Observation Network of China (CMONOC) in 2018. Secondly, the RIM TEC data are used for forecasting research.   Results:  During the quiet period and disturbance period, the best time scale for training set is 21 days. For the 5-day sliding prediction, the root mean square error(RMSE) of the PSOENN model during the quiet period and disturbance period are decreased by 27.6% and 20.5%, respectively. When using the global ionospheric map (GIM) published by CODE in the China region for different grid points prediction, the average RMSE of the PSO-ENN model is lower than the CODE'S 1-day predicted GIM(C1PG) by back propagation neural network (BPNN) model and ENN model. For the 30-day sliding prediction in August 2018 based on RIM TEC in China region, the average RMSE of the PSO-ENN model are 24.8% and 14.3% lower than that of BPNN and ENN model, respectively.   Conclusions:  The model established in this paper has better prediction accuracy and stability both in quiet period and disturbances period.