The work aims to propose a reconstruction method of compressed sensing image based on generative ad-versarial network, in order to solve the problems of long reconstruction time and low quality of reconstructed image by traditional compressed sensing image reconstruction method. Based on the idea of generative adversarial network, a deep learning network model composed of discriminator with sparse sampling function and generator with image reconstruction function was designed. The new loss function composed of adversarial loss and reconstruction loss was used to optimize the network parameters and complete the process of image compression and reconstruction. Experiments showed that the reconstruction time required by the proposed method was 0.009 s at a low sampling rate of 12.5%, and the Peak Signal to Noise Ratio (PSNR) was 10-12 dB higher than that of the commonly used OMP algorithm, CoSaMP algorithm, SP algorithm and IRLS algorithm. When applied to image reconstruction, the proposed method require less reconstruction time and can still achieve a high-quality reconstruction effect at a low sampling rate.