Aimed at the problems that the convolutional neural netwok (CNN) method in polarimetric SAR image classification is long in training time, and slow at convergence speed, and the original Softmax function cannot effectively deal with the intra class differences of polarimetric SAR images, a model based on finetuning and addinga polarimetric SAR image classification method is proposed by Additive Margin Softmax (AM-Softmax). This method improves the efficiency and classification accuracy of the CNN model through the overall fine tuning of the pre trained network, and then replaces the Softmax with AM-Softmax to solve the problem of large intra class variationin SAR images and further improve the classification accuracy. The experiments show that this method is fast on convergence and deal with the problems of large variation in polarization SAR images within a class, and the overall classification accuracy of the model reaches above 96%.