In this work, three supervised machine learning algorithms, XGB, FNN and XGB-FNN, are employed to train and predict thermal rate constants of important combustion reactions between alkanes and free radicals (CH3, H, O). The cosine similarity between the reactant alkane and the resulting alkyl radical combined with the screened topological indices of alkanes are taken as the reaction descriptors to feed the machine learning models. The hybrid XGB-FNN algorithm is found to perform better than the XGB and FNN algorithms on predicting thermal rate constants. The prediction accuracy of the similarity-based XGB-FNN model is comparable to that using the activation energy as a descriptor. Different from the activation energy, the similarity can be directly generated from the reactant and product fingerprints and doesn't require extra quantum chemistry calculations. The XGB-FNN models have ability to predict thermal rate constants of hydrogen abstraction reactions from different sites of alkanes and their isomers. The newly designed reaction representations endow machine learning with great potential in developing reliable models to predict thermal rate constants. It's our hope that these reaction descriptors can be applied to develop other quantitative structure-property relationship models relevant to gas-phase chemical reactions.