Rapid discriminant analysis of meat and bone meal from different animal origin species is an important guarantee to strengthen feed supervision and prevent the spread of BSE disease. In order to explore the feasibility of using advanced micro-computed tomography (Micro-CT) to rapidly identify and analyze meat and bone meal from different species of animals, a calibration sample set and two validation sample sets consisted of different amount of avian origin and bovine bone particles were prepared. The Bruker Skyscan 1275 Micro-CT system was used to build method for 3D in situ visual characterization. The Micro-CT conditions for sample scaning and images reconstructing were: tube voltage of 80kV, tube current of 125μA, image resolution of 10μm, reconstructed gray-scale image of from 0 to 255, and the corresponding X-ray absorption coefficient was from 0 to 0.035. The regions of interest of different bone particle samples were extracted for image segmentation. Combined with PLS-DA and SVM-DA machine learning algorithms, avian origin and bovine origin bone particle image segmentation models were constructed, respectively. Finally, the Micro-CT in situ 3D visual discriminant analysis of avian origin and bovine bone particles were carried out. The main results were as follows: the gray range of the regions of interest for image segmentation of chicken and bovine bone particles was 165~255. The total accuracy of cross validation of chicken and bovine bone particles based on PLS-DA and SVM-DA models was 94%. The Micro-CT 3D in situ visualization results of the verification set samples were verified to be consistent with the actual results of the samples. The verification results showed that the established Micro-CT 3D in situ visual discriminant analysis method achieved very consistent results with that of the actual samples. The research result can provide an imaging methodology for 3D in situ visual discriminant analysis for rapid and non-invasive identification of different species of animal origin material in feed.