Effective prediction of the degree of ash in the heated area of the boiler can provide an important basis for boiler production efficiency and fault early warning. The cleaning factor is used to evaluate the ash deposition status of the heated surface, and according to the characteristics of nonlinearity and non-stationariness of the sequence, a method of predicting the heated area ash based on the empirical modal decomposition and time convolutional network of complementary sets is proposed. Firstly, the original sequence after wavelet threshold denoising is decomposed into a set of sub-sequence components by complementary set empirical mode decomposition, then the time series prediction model based on the time convolutional network is constructed for different sub-sequences, and the network hyperparameters are optimized to improve the prediction accuracy; finally, the prediction results of each IMF component are superimposed to obtain the prediction values of the cleaning factor. Compared with the other two models, the prediction accuracy is improved by 62. 1% and 57. 1%, respectively, and the CEEMD-TCN model has the highest prediction accuracy for the ash condition of the heated area, which verifies the accuracy and reliability of the model.