The safety prediction of rail transit system is a fundamental problem in rail transit modeling and management. In this paper, we propose a safety prediction model based on deep learning for rail transit safety, which has been implemented as a deep belief network (DBN). It can learn effective features for rail transit prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To increase the accuracy of prediction, we introduce user satisfaction and rare-event probability, the new input prediction factors, into safety prediction. The former takes account of human and the latter is computed by statistic model checking. To show proof of the model, a real-world subway data sets based on the Beijing Metro in China is presented to demonstrate the feasibility of the model. Experiments on data sets show good performance of our prediction. These positive results demonstrate that deep learning and new factors are promising in rail transit research.