For the synthetic aperture radar (SAR) target classification method, a multi-view was developed based on the principle of the maximum entropy. The mutual-correlation matrix between multi-view SAR images was established based on the classical image correlation. Afterwards, the nonlinear correlation information entropy (NCIE) of different view sets was calculated. NCIE is capable of analyzing the statical properties of multiple variables and entropy value reflects the inner correlation of different variables. The view set with the highest nonlinear correlation information entropy was chosen, in which the multiple views share the highest correlation. The joint sparse representation was employed to represent the selected multi-view SAR images and the target label was determined based on the total reconstruction errors. The joint sparse representation is capable of handling several sparse representation problems and enhancing the reconstruction precision when these problems share some correlations. The proposed method could effectively analyze the inner correlations of multiple views and employ joint sparse representation to exploit such correlations so the classification accuracy can be improved. Typical experimental setups were designed based on the MSTAR dataset to test the performance of the proposed method while compared with some other methods under different test conditions. The results show the validity of the principle of the maximum entropy and the superior performance of the proposed method for SAR target classification.