Accurate determination of activation energy by quantum chemistry calculations has often been challenging for complex polyatomic reactions. In addition, if there exist multiple reaction pathways, it is difficult to choose the calculated activation energy used in the models. In this work, we intend to design new molecular descriptors to represent hydrogen abstraction reactions in combustion, in the condition that, on one hand, the descriptors don't require extra quantum chemistry calculations, and on the other hand, the prediction accuracy of machine learning methods based on the new descriptors archives the accuracy with the activation energy as a descriptor. Concretely speaking, the rate constants of hydrogen abstraction reactions between alkanes and radicals (CH3, H and O) will be independently trained by combining different machine learning models with newly designed descriptors and then these models are applied to predict rate constants of combustion reactions with the number of involved carbon atoms in alkanes up to 16. It is noteworthy that fuels with a carbon chain length of C8-C16 are the main constituent of aviation kerosene.