Abstract: This study aims to comprehensively characterize the composition of meat and bone meal, and further to identify the specific variables of various species using a data mining method. Based on the component characteristics data, a comprehensive comparison and markers mining study were carried out for the meat and bone meal that produced by various species. 166 samples of meat and bone meal were produced from various species (55 swine, 43 poultry, 36 bovine, and 32 ovine) in different factories of China. Composition characteristics in the samples of meat and bone meal were detected from four aspects, including the proximate component, element, fatty acid, and amino acid composition. The results of proximate component show that there was a complex variation in the samples of meat and bone meal, leading to the difference in four species was not considered statistically significance. An one-way Anova variance analysis was conducted for the composition data of element, fatty acid, and amino acid. 69 component variables were compared, incuding 14 variables from element composition, 37 variables from fatty acid composition, 18 variables from amino acid composition, in the meat and bone meal from different species. Consequently, there were significant differences among species (P<0.05) for 31 component variables, including 10 variables from element composition, 20 variables from fatty acid composition, 1 variable from amino acid composition. It infers that the component characteristics of meat and bone meal varied significantly in different species, particularly on the specific component variables. A Principal Component Analysis (PCA) combined with the Partial Least Square Discrimination Analysis (PLS-DA) was used to explore the species specificity of meat and bone meal. The results showed that the composition variables of element and fatty acid can serve as markers to idnetify the swine, poultry, bovine, as well as ovine meat and bone meal. The composition variables of amino acid were mainly marker sources of ruminant and non-ruminant meat and bone meal. Comprehensively characterization using the PLS-DA and one-way Anova variance analysis demonstrated that, taking the VIP value greater than 1, while P < 0.05 as the united indicator, the specific variables were achieved in the meat and bone meal for the species of: 1) swine were C10:0, C18:0 and C18:2n6c, 2) poultry were Ca, K, Zn, C18:0 and C18:2n6c, 3) bovine were Sr, C14:1, C17:0, C17:1, C18:0 and C18:2n6t, 4) ovine were H, Mg, Sr, C10:0, C16:0, C17:0, C17:1 and C18:0, 5) ruminant and non-ruminant were Sr, Ba, C14:1, C17:0, C15:0, C17:1, C18:0, C18:2n6t, C18:2n6c and serine, and 6) mammal and non-mammal were K, Zn, C18:0 and C18:2n6c. These selected specific variables can provide: a sound theoretical basis for the multi-application of meat and bone meal from different species. The finding can also offer sinificant data support for the mechanism analysis and further application of meat and bone meal, particularly on the species identification method.