Question Generation aims to create various questions from a given passage, which can provide education material, improve the training of question answering, and help chat bots have cold-to-start or continue to talk to people. Chinese Question Generation is a new research area, which mainly uses a rule-based model to convert declarative sentences into questions. We propose a Stroke-Aware Copy Network (SAC-Net), which is a sequence-to-sequence model. It can enhance the performance of Chinese question generation by using various specific components and techniques for Chinese characteristics. In view of the fact that the morphology of Chinese characters is related to its meaning, this research introduces the information of strokes in Chinese and obtain the relationship among strokes in Chinese characters. This model introduces the direction information of the questions, which helps us generate more diverse questions. For the out-of-vocabulary (OOV) word problem that often occurs in Chinese, this research has improved a Self-attention Copy mechanism. Extensive evaluations confirm that this model improves the performance of Chinese problem generation significantly and represents a state-of-the-art neural Chinese problem generation model.