Calorific value is an important indicator to evaluate the comprehensive quality of coal, and its real-time and rapid analysis is of great significance for optimizing the coal blending process and improving boiler combustion efficiency. Traditional assays are time-consuming, and prompt gamma neutron activation analysis (PGNAA) and laser-induced breakdown spectroscopy (LIBS) have certain limitations. In this paper, a novel technique for ultra-repeatability measurement of coal calorific value by combining near-infrared spectroscopy (NIRS) and X-ray fluorescence (XRF) is proposed. In this NIRS-XRF technology, the former can stably measure organic components such as C-H and N-H that are positively correlated with the calorific value, while the latter can stably measure inorganic elements such as Na, Al, Si, Ca, Fe, and Mn that are negatively correlated with the calorific value. The combination of the two can greatly improve the measurement repeatability of coal calorific value. In the quantitative analysis algorithm, a holistic-segmented prediction model based on partial least squares (PLS) is proposed, that is, the holistic model is used to roughly predict the calorific value and determine the segment accordingly, and then the corresponding segmented model is used to accurately predict the calorific value. The experimental results show that the root mean square error of prediction (RMSEP), the average relative error (ARE), and the standard deviation (SD) of this method for predicting the calorific value of coal are 0.71 MJ kg-1, 1.18% and 0.07 MJ kg-1 respectively. The measurement repeatability meets the requirements of the Chinese national standard. This calorific value measurement technology based on NIRS-XRF is safe, fast, and stable, providing a new way to optimize and control the utilization process of coal in coal washing plants, power plants, coking, and other industries.