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Low-field nuclear magnetic resonance (NMR) is widely used for accurate characterization of coal pore structure. The NMR T 2 spectrum represents the pore size distribution. Also, the T 2 cutoff (T 2c) value is a key parameter, reflecting the free/bound fluid proportion of coal. To characterize the pore structure of coal more comprehensively, the NMR T 2 spectra of coals with different pore structures were characterized by multifractal, and then, the T 2c values were predicted by a BP neural network model. The main conclusions are as follows: the T 2 spectra of three ranks of coals (anthracite, bituminous coal, and lignite) showed typical unimodal, bimodal, and trimodal distributions, respectively. The porosity had a weak negative correlation with T 2c, whereas the proportion of free fluid had a strong negative correlation with T 2c. The quality indices τ­(q) of the three coals changed monotonously, which conformed to the multifractal characteristics. The generalized fractal dimension spectra decreased in an inverse S-shape, decreasing while the multifractal singular spectra were hook-shaped. D min, ΔD, αmax, α0, and Δα showed strong positive correlations with T 2c, which indicated that with an increase in T 2c, the proportion of large-size pores decreased and the local pore size distribution became more concentrated and inhomogeneous. The predicted T 2c values of the training, verification, and test sets of the BP neural network model fitted the measured T 2c values well, and the mean square error was only 0.17%. The trained BP neural network model was reliable and can be used for the T 2c prediction of more similar coal samples.