In order to solve the problem that power plant boiler operators rely on experience to adjust boiler operating parameters to reduce the NOx concentration at the SCR inlet and improve the denitration performance, a method for predicting the NOx concentration at the SCR inlet is proposed. This method establishes a CNN(1D)-LSTM model based on convolutional neural network and long short-term memory neural network. The NOx concentration at the SCR inlet can be predicted after 5 min. Power plant operators can use the prediction results of the model as an important reference for the NOx concentration at the SCR inlet, and more effectively adjust boiler parameters for denitrification optimization. The results show that the LSTM model for predicting the NOx concentration at the SCR inlet after 3 min is better than CNN(1D)-LSTM; the CNN(1D)-LSTM model for predicting the SCR inlet concentration after 5 min has a great prediction accuracy compared with the LSTM model. The Emape on the test set is 7.05%. The desired effect was achieved.