In view of the fact that the ash in the heated surface of the boiler will reduce the heat transfer efficiency and safety, uses the cleanliness factor as a healthy indicator to monitor the health of the heated surface of the boiler, and proposes a model that combines empirical mode decomposition (EMD) and long short-term memory (LSTM) to predict future boiler ash deposit. EMD can decompose a time series into a series of intrinsic mode functions which are stable in frequency domain, both LSTM has a memory function, it can learn to mine hidden long-term dependencies between time series, the combination of the two increases the accuracy of time series prediction. It is verified by simulation software that the model has satisfactory accuracy in the prediction of the ash condition of the heated surface of the boiler, and compared with two commonly used models, it was found that the prediction accuracy increased by 67. 7% and 59. 2% respectively and the feasibility and validity of the model are verified.