Ash deposition on the heating surfaces of coal-fired power station boilers causes a significant decrease in energy efficiency. If it accumulates continuously, it may cause serious safety accidents. Therefore, accurate prediction of the energy efficiency of the boiler heating surface is an important way to solve this problem. To achieve the improvement of energy efficiency prediction accuracy, this paper constructs an energy efficiency prediction model for coal-fired power station boilers based on a neural basis expansion analysis (NBEATS) model. This model is optimized by the honey badger algorithm (HBA) that incorporates a two-step decomposition strategy. This method uses the cleanliness factor as an indicator of energy efficiency in the boiler heating surface, and applies wavelet threshold theory to denoise the original cleanliness factor data. The two-step decomposition strategy of robust empirical mode decomposition (REMD) combined with variational mode decomposition (VMD) is used to obtain multiple mode functions of the denoised data. For each decomposed mode function, the HBA algorithm is applied to tune the hyperparameters of the NBEATS model for independent prediction, and the predicted values of all mode functions are superimposed to generate the boiler energy efficiency prediction results. Experiments on the boiler dataset of a 300 MW coal-fired power station in Guizhou Province show that the proposed model outperforms the ablation model in multiple performance metrics, the root mean square error performance is improved by 5.43%~93.31%; the coefficient of determination increases to above 0.98; the minimum mean absolute error achieves 0.000 2.