The calorific value of coal ash is an important indicator to evaluate the coal quality. In the experiment, the effect of spectrum and processing methods such as smoothing, differential processing, multiplicative scatter correction (MSC) and standard normal variate (SNV) in improving the near-infrared diffuse reflection spectrum signal-noise ratio was analyzed first, then partial least squares (PLS) and principal component analysis (PCR) were used to establish the calorific value model of coal ash for the spectrums processed with each preprocessing method respectively. It was found that the model performance can be obviously improved with 5-point smoothing processing, MSC and SNV, in which 5-point smoothing processing has the best effect, the coefficient of association, correction standard deviation and forecast standard deviation are respectively 0.989 9, 0.000 49 and 0.000 52, and when 25-point smoothing processing is adopted, over-smoothing occurs, which worsens the model performance, while the model established with the spectrum after differential preprocessing has no obvious change and the influence on the model is not large.