Autoencoder, as an important branch of deep learning, has appealed many outstanding researchers in this field. Researchers studied its essence, and proposed many optimized approaches, such as sparse autoencoder, denoising autoencoder, contractive autoencoder, and convolutional autoencoder. After reading a number of articles on autoencoder methods, we found that the optimized autoencoder had sound experimental results in terms of image classification, natural language processing, and object recognition. Therefore, this review analyzes the basic principle and structure of optimized autoencoder in details. In addition, the multi-perspectives evaluation and analysis on the experimental results in literatures are carried out as well.