Chinese text proofreading is one of the key tasks in Chinese natural language processing, and manual proofreading is difficult to meet the data volume requirement of daily work, and the text proofreading method based on statistics can not deal with semantic errors flexibly. Aiming at the above problems, a Chinese text proofreading method based on neural network and attention mechanism is proposed. The bidirectional Gated Recurrent Unity neural network layer is used to obtain text information and feature extraction, and the ability of attention mechanism layer to enhance the semantic logic relation between words is introduced. The model is implemented under the framework of deep learning based on Keras. Experimental results show that this method can proofread text with semantic errors.