With massive features and long unit delays, it is very difficult to effectively select the most appropriate features and corresponding delays during the modeling of the main steam temperature of thermal power unit. Therefore, a modeling method of the fusion model jointly considering feature selection and delay selection was proposed. Aiming at the high dimensionality of the features of thermal power units, the features highly associated with the main steam temperature were selected through the correlation coefficients and the feature selection of gradient boosting machine. For the delay identification, the Temporal Correlation Coefficient-based Time Delay(TD-CORT) calculation algorithm was designed to estimate the time delay between each parameter and the predicted target main steam temperature. And the automatic matching of the sliding window size was realized for the prediction target and the calculation complexity. Finally, the fusion model of Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) was used to predict the main steam temperature of the thermal power unit. The deployment results on a 1 000 MW ultra-supercritical coal-fired unit in China show that the proposed method has the prediction Mean Absolute Error (MAE) value of 0.101 6, and the prediction accuracy 57.42% higher than the neural network without considering the delay.