It is important to know the calorific value of coal in real time for adjusting the ratio of air to powder of power plant boilers and increasing the coal combustion efficiency. Currently， power production is in urgent need of a rapid， highly stable calorific-value detection method. Therefore， this paper innovatively proposes a highly stable detection method for the coal calorific value using the combination of near-infrared spectroscopy （NIRS） and X-ray fluorescence （XRF） spectroscopy， which significantly improves the measurement repeatability of the coal calorific value by combining the advantages of NIRS for highly stable detection of organic groups positively related to the calorific value in coal and XRF spectroscopy for ash-forming elements negatively related to the calorific value. The spectral preprocessing method used in this study involves fusing the two sets of spectra as input variables for partial least squares regression （PLSR） for preliminary modeling of the full spectrum， selecting the effective bands in the NIRS spectrum according to the regression coefficients， and then fusing them with the ash-forming element XRF spectra for normalization. All the data of the preprocessed fused spectra are used as input variables to model the coal calorific value using PLSR. The experimental results indicated that the linear correlation coefficient （R²） of the present NIRS-XRF coupled method for the prediction of the calorific value of the calibration set coal samples was 0.995， and the minimum root-mean-square error， average relative error， and standard deviation for the prediction of the calorific values of the validation-set coal samples were 0.24 MJ/kg， 0.61%， and 0.05 MJ/kg， respectively. The measurement repeatability fully satisfied the requirement of <0.12 MJ/kg based on the national standard. The proposed highly stable detection method combining NIRS and XRF spectroscopy for the coal calorific value is expected to be popularized and applied in high carbon industries such as thermal power generation， the coal chemical industry， metallurgy， cement， coking， etc.， to help China achieve carbon neutrality on schedule.