In order to protect the image privacy and solve the problem that optical image encryption relies on high precision of optical instrument, an optical image encryption method based on an autoencoder is proposed. In this method, a deep neural network is used to simulate double random phase encoding. The target random image added into the input is used for simulating the first random phase template, and the convolution kernel of the encoding network is used for simulating the second layer random phase template. The experimental results of satellite images show that this method can effectively encrypt optical images, and the pixel distribution of ciphertext images on histograms is relatively uniform. Compared with the encryption method based on CycleGAN, this method is simpler and consumes less computing resources. The peak signal-to-noise ratio (PSNR)value of the encrypted image decreases by 6.5745, and the absolute values of the correlation coefficients of adjacent pixels in the horizontal, vertical, and diagonal directions decrease by0.110375, 0.118625, and0.01335, respectively. The PSNR value of the decrypted image increases by 1.4075, and the structural similarity value increases by 0.0428.