It is of great social value and cultural and technological innovation demonstration value to carry out the research on the scientific identification method of painting and calligraphy works of art, and it is of great effect to the trade, collection, and protection of painting and calligraphy works of art. Spectral imaging, as an information acquisition method of attribute and visual synchronous perception, can be used for painting and calligraphy identification. In particular, through hyperspectral imaging and data analyses, we can identify the pigment ink used in painting, judge the printing characteristics, and find the painting information invisible to human eyes, to comprehensively judge the authenticity and abnormality of painting. However, due to its lack of matching painting and calligraphy identification model and algorithm, as well as special painting and calligraphy atlas database support, it is difficult to be competent for rapid and accurate identification in practice. Because of the above problems, in this paper, it is simulated the expert identification process for artificial intelligence analysis and modeling, adopts the idea of combining hyperspectral imaging and Atlas intelligent learning, proposes a method of atlas feature extraction for calligraphy and painting identification, and designs and studies convolution neural network(CNN) based on atlas feature, traditional image feature, and the mixed feature of atlas and image, to judge the authenticity of calligraphy and painting, the author and so on. The actual test results show that the convolution neural network based on the atlas features is the best, the author classification accuracy and authenticity identification rate in the test sample set are more than 96.5%, and it can also be seen that in the pseudo color image data, adding multivariate spectral features can greatly improve the accuracy significantly.