The paper aims to complete image compression and reconstruction through deep learning supplemented by Laplacian pyramid. Main features of the image were extracted with the convolutional neural network. The feature size was reduced by the bicubic linear interpolation. The hierarchy system was constructed by Laplacian pyramid to gradually reduce the image size and achieve image compression. On the reconstruction end, the corresponding convolution and up-sampling process was performed on the system; and the image reconstruction and reconstruction process was performed to obtain a reconstructed graph. Set 5 and set 14 from Bell Laboratories in France were used for verification. The experimental results were verified by the two-layer pyramid, which meant that the experimental results were verified at the 16 times of high-rate compression. The results showed that the method of deep learning was superior to PCA, DCT and SVD in terms of clarity and reduction in subjective evaluation, and the best results of standard deviation (52.73) and information entropy (7.44) were obtained in objective evaluation, which were higher than 49.70 and 7.38 of PCA. The standard deviations of SVD transform and DCT transform were only 48.69 and 49.02, which were far worse than the methods in this paper. Meanwhile, the information entropy of images was only 7.34 and 7.35, which was lower than 7.44 in this paper. Design convolutional neural network structure by Laplacian pyramid to complete image compression and reconstruction achieves good results.