Stacked auto-encoder is mainly used for image classification and it can extract valid information from data through unsupervised pre-training and supervised fine-tuning. This paper is intended to improve the accuracy of image classification, we constructed a 6-layer stacked convolution neural network (CNN) based on stacked auto-encoders. The constructed CNN can extract effective features for image classification through greedy layer-wise training. In order to make the constructed CNN to have strong robustness to noise, we added a noisy-layer in the pre-training stage. Adding the sparsity constraint can make the training of the CNN more effective, and can also reduce data redundancy. For classification applications, our experiments show that the final classification results of the proposed model is superior to the combination of auto-encoders and the noisy auto-encoders.