A substance identification device for cosmic ray imaging, comprising: a classifier establishing unit configured to establish a classifier based on scattering density values reconstructed for a plurality of known sample materials, wherein the classifier comprises a plurality of feature regions corresponding to a plurality of characteristic parameters for the plurality of known sample materials, respectively; and an identification unit for a material to be tested, configured to match the characteristic parameter of the material to be tested with the plurality of feature regions by using the classifier, to determine a feature region corresponding to the characteristic parameter of the material to be tested from the plurality of feature regions, and to identify a type of the material to be tested based on the feature region corresponding to the characteristic parameter of the material to be tested; characterized in that, wherein the classifier establishing unit comprises: a noise reduction processing module configured to perform a noise reduction process on the scattering density values reconstructed for each of the plurality of known sample materials; and a cluster analysis module configured to perform a cluster analysis on the scattering density values processed by the noise reduction process for each of the plurality of known sample materials, so as to obtain a distribution feature of the scattering density values for each of the plurality of known sample materials, wherein the distribution feature comprises a Landau distribution, and the characteristic parameter comprises a position parameter being a horizontal coordinate of a highest point of the distribution and a scale parameter describing a spread of the distribution. a classifier establishing unit configured to establish a classifier based on scattering density values reconstructed for a plurality of known sample materials, wherein the classifier comprises a plurality of feature regions corresponding to a plurality of characteristic parameters for the plurality of known sample materials, respectively; and an identification unit for a material to be tested, configured to match the characteristic parameter of the material to be tested with the plurality of feature regions by using the classifier, to determine a feature region corresponding to the characteristic parameter of the material to be tested from the plurality of feature regions, and to identify a type of the material to be tested based on the feature region corresponding to the characteristic parameter of the material to be tested; characterized in that, wherein the classifier establishing unit comprises: a noise reduction processing module configured to perform a noise reduction process on the scattering density values reconstructed for each of the plurality of known sample materials; and a cluster analysis module configured to perform a cluster analysis on the scattering density values processed by the noise reduction process for each of the plurality of known sample materials, so as to obtain a distribution feature of the scattering density values for each of the plurality of known sample materials, wherein the distribution feature comprises a Landau distribution, and the characteristic parameter comprises a position parameter being a horizontal coordinate of a highest point of the distribution and a scale parameter describing a spread of the distribution. a noise reduction processing module configured to perform a noise reduction process on the scattering density values reconstructed for each of the plurality of known sample materials; and a cluster analysis module configured to perform a cluster analysis on the scattering density values processed by the noise reduction process for each of the plurality of known sample materials, so as to obtain a distribution feature of the scattering density values for each of the plurality of known sample materials, wherein the distribution feature comprises a Landau distribution, and the characteristic parameter comprises a position parameter being a horizontal coordinate of a highest point of the distribution and a scale parameter describing a spread of the distribution.