Chinese character recognition is an important research content in the field of artificial intelligence and pattern recognition. Existing research still has problems such as difficulty in parameter adjustment, small number of training samples, and inability to identify all common characters. Aiming at these problems, we propose a Chinese character recognition method based on character encoding and convolutional neural network. First, we obtain all the character information by querying the font database, which are encoded and outputted by using UTF8 encoding method and various font encoding files to generate character images. Further, we apply various of image processing to obtain the new character image dataset. Then, we propose a deep convolutional neural network for Chinese character recognition. In the training procedure, data augmentation, batch normalization, RMSProp optimization are optimized, regularization and dropout are used to prevent overfitting for optimization. The experimental results show that the proposed method is simple yet effective, the recognition accuracy rate for Chinese characters is 9808%. Compared with Alexnet and LeNet5, we obtain a significant improvement by 937% and 2114%. A neural network with high recognition rate, strong feature extraction ability and generalization ability is realized.