The pyrolysis characteristics of nine kinds of Chinese coals obtained by the thermogravimetry were modeled using artificial neural network. In order to remedy the defects of back propagation algorithm, a momentum term and an adaptive learning rate were introduced. The architecture of the improved back propagation neural network model was 3 x 5 x 3, which included three input nodes (content of volatiles and ash, C/H), five hidden nodes, and three output nodes (the weight loss percentage, the maximum weight loss velocity temperature, and the weight loss peaks). The model prediction results indicated that the modified back propagation neural network had a fast convergence speed and a high accuracy. Moreover, the maximum relative error was less than 4%. Furthermore, in model analysis the effect of each input node was quantized by the sum of absolute values of weights. And the results indicated that C/H had more remarkable influence than the other two.