This study designs a new generator network, a new discriminator network, and a new loss function for image scene conversion. First, the generator network uses a deep convolutional neural network with a skip connection structure, in which multi-skip connection is used to share the structure information of the image. For the discriminator network, it uses a multi-scale global convolutional network which can distinguish between real and generated images of different sizes. At the same time, the new loss function is a combination of four loss functions referring to other algorithms, including GAN loss, L₁ loss, VGG loss, and feature matching loss. Moreover, the validity of the new loss function is demonstrated through experimental comparisons. The experimental results show that the proposed algorithm can achieve multi-image transformations, and the details of generated images are preserved completely, the generated image is more realistic, and the block effect is obviously eliminated.