Abstract: Aiming to improve the prediction accuracy of the concentration of nitrogen oxides (NOx) in the flue gas at the outlet of selective catalytic reduction (SCR) system for coal-fired power plants, a prediction model method based on the maximum information coefficient (MIC) and long-short term memory (LSTM) neural network was proposed. Firstly, MIC was used to estimate the delay time between various input parameters and the recorded NOx concentration, and the data were reconstructed according to the estimated delay time. Then the MIC value of the reconstructed data was used as an index to evaluate the correlation between input variables and output variables, and the correlation-based feature selection (CFS) algorithm was used to select the input variables. Finally, based on the data after time delay reconstruction and variable selection, the dynamic prediction model of NOx concentration at SCR outlet was established using LSTM neural network. The model was used to analyze the recorded operation data of a 320 MW coal-fired unit in Guangdong. The results show that the LSTM prediction model established after time delay reconstruction and variable selection has high accuracy, superior to deep neural networks (DNN) model and radial basis function (RBF) model, with the mean absolute percentage error of 2.58% and the root mean square error of 2.02, which can meet the requirements of field application.