Based on the output data’s reconstructing the original data, classical auto encoders(BAE、SAE、DAE、CAE) can extract low dimensional features of the input information.Applying these features in image classification may not able to guarantee a good result.In this paper,we use the label information to propose a stacked discriminant auto encoder (SDcAE),which adds the class encoderas the constraint of hidden layer neuron into the training process of the stacked auto encoder. Hence, the features learned by the hidden layer have better discrimination ability. In addition,we propose a class encoding classifier(CEC),which adds the class encoder as the discriminative loss into the Softmax classifier.Due to the decrease of the feature error of the interclass samples, the classifier can achieve better training results, thus improving the final classification accuracy.The experimental results show that the stacked discriminant auto encoder and the class encoding classifier are effective and feasible in image classification.