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• Functional groups are effective variables of machine learning for FTIR spectra. • OH is a key factor discriminating the samples because of deoxidation reactions. • Pre-integration of initial samples increases the classification effects of PCA. Three biomass (rice straw, rice husk and wheat straw) and three coal samples (lignite, subbituminous coal and bituminous coal) were extracted via a thermal dissolution (TD) process, and the TD extracts were treated by catalytic hydrogenation to obtain reaction products. To reveal the similarity in molecular information among the samples and elucidate their chemical reactivity, four machine learning algorithms were applied to analyze the Fourier transform infrared spectra of both TD extracts and catalytic hydrogenation products. Functional groups were used as variables and the difference in peak area can be treated as the basis for sample classification. Aromatic C H, C O C, aliphatic CH 2 or CH 3 and aromatic C O or C C bonds were the main characteristic variables in principal component analysis algorithm to classify biomass- and coal-derivated samples. These samples were also grouped into four clusters by hierarchical clustering analysis algorithm according to the similarity and difference in the distribution of functional groups. For artificial neural network algorithm, aliphatic C H and OH bonds are the most important variables to classify these samples into four groups, and aromatic C H, OH, and C O C groups are the main variables contributed to the classification trees in random forest algorithm. Machine learning algorithms will provide methodological guidance for the data mining of the spectra of complex organic systems. [ABSTRACT FROM AUTHOR]