We have developed a new method to suppress spontaneous combustion of coal piles by covering the surface of coal piles with pulverized coal. Experimental studies of three type of coal samples from China (YJL, CYW, and SW) with particle size ratio of 10:1 were performed to investigate the low-temperature oxidation of coal pillars. In this work, we have also demonstrated that the distributions of oxygen concentration, the temperature field, as well as the spontaneous combustion of three typical Chinese coal samples can be predicted accurately using back-propagation neural network (BPNN) by MATLAB. Pearson correlation analysis showed that temperature and oxygen concentration highly depend on the ratio of pulverized coal thickness to coal piles thickness, activation energy, void ratio, wind speed, and low-temperature oxidation time. Three-layer BPNN models with five input factors were developed to predict the low-temperature oxidation process under pulverized coal. The prediction data of BPNN are fitting better with our experimental data, which confirms that BPNN modelling can accurately predict the low temperature oxidation process of coal.