As data sets and data streams continue to expand, traditional machine learning is becoming less effective in predicting fake news. This paper is a review of deep learning in fake news detection and prevention. Author takes the model based on convolutional neural network as an example to illustrate the principle and application of deep learning in fake news detection, including OPCNN-FAKE, Dual-channel Convolutional Neural Networks with Attention-pooling (DC-CNN) model which is completely based on Convolutional Neural Network (CNN), and Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) model which combines convolutional neural network with long-short time model. These models have obvious advantages in accuracy over traditional machine learning models. This paper then points out the problems of deep learning in the field of fake news identification: it does not have good scalability and slow training speed. The author proposes possible solutions, and widely uses transfer learning and uses distributed computing platforms, such as spark, to train models. Hope this review can help the research on fake news prediction using deep learning.