The automatic generation of Chinese calligraphy images is a very challenging task, because the structure of Chinese characters is very complex. At present, most methods learn the style of the images one by one, meaning that they lack the ability to model the style of the calligraphy from a more macro perspective. To solve these problems, this paper proposes a one-to-many style transfer model, SCGAN, based on a style collection mechanism. Our model can gather information from the collection level to complete the task of Chinese character image generation. The main features of our model are as follows: first, based on the proposed style collection mechanism, our model can collect and transform style features from the collection level; second, we redesigned the structure of the generative adversarial network. Our model can complete the one-to-many style transfer task, which can greatly reduce the workload associated with multi-target style transfer. Compared with other deep learning methods, the results obtained by our method are higher quality and closer to reality. Experimental results show that our method achieves better performance than other methods in one-to-one and one-to-many Chinese character generation tasks.