The carbohydrate composition (glucose, xylose, mannose, galactose, and arabinose) of lignocellulosic biomass Liriodendron tulipifera, Populus nigra × Populus maximowiczii, Populus alba × Populus glandulosa, Populus euramericana, Salix alba, Quercus variabilis, Robinia pseudoacacia, Zelkova serrata, Abies holophylla, Pinus rigida, rice straw, and peanut hull was investigated based on high-performance liquid chromatography (HPLC) and gas chromatography (GC) analyses derived from ASTM and NREL methods. The glucose content was higher in HPLC than in GC analysis, and the xylose, mannose, galactose, and arabinose contents were higher in GC than in HPLC analysis. The difference in carbohydrate composition was noticeable in the glucose, mannose, and arabinose contents of Abies holophylla and Pinus rigida, and this was affected by the species. A decision tree, as a data mining and artificial intelligence method, is a reliable and simple variable selection tool. This technique was used for carbohydrate analysis classification. Accordingly, 432 monosaccharide content reading data and analysis methods were used for model checking. It was found that arabinose was the most important splitting variable in carbohydrate analysis, and other monosaccharides did not influence the assay decision. However, the selection of a determination method for each sample should be considered comprehensively in future studies.