Concerning the problems of poor reconstructing performance with large-scale factors and requirement of separate training in image reconstruction with different scales in current image super-resolution reconstruction algorithms, an image super-resolution reconstruction algorithm based on Laplacian pyramid Generative Adversarial Network(GAN) was proposed. The pyramid structure generator of the proposed algorithm was used to realize the multi-scale image reconstruction, so as to reduce the difficulty in learning large-scale factors by progressive up-sampling, and dense connection was used between layers to enhance feature propagation, which effectively avoided the vanishing gradient problem. In the algorithm, Markovian discriminator was used to map the input data into the result matrix, and the generator was guided to pay attention to the local features of the image in the process of training, which enriched the details of the reconstructed images. Experimental results show that, when performing 2-times, 4-times and 8-times image reconstruction on Set5 and other benchmark datasets, the average Peak Signal-to-Noise Ratio(PSNR) of the proposed algorithm reaches 33.97 dB, 29.15 dB, 25.43 dB respectively, and the average Structural SIMilarity(SSIM) of the algorithm reaches 0.924, 0.840, 0.667 respectively, outperforming to those of other algorithms such as Super Resolution Convolutional Neural Network(SRCNN), fast and accurate image Super-Resolution with deep Laplacian pyramid Network(LapSRN) and Super-Resolution GAN(SRGAN), and the images reconstructed by the proposed algorithm retain more vivid textures and fine-grained details in subjective vision.