Urban public traffic networks have been carrying huge passenger flow all the time. And the increase of passenger flow brings great pressure to public traffic networks and traffic intelligent dispatch. Shortterm passenger flow forecasting on subway station provides an important technical support for decisionmaking in the intelligent subway dispatch system. With the historical data of metro cards, we propose a shortterm passenger flow prediction method based on deep learning which is able to extract inherent and deep features from data. So a deep learning network model is built based on stacked autoencoder (SAE) and we pretrain the model in a downtop fashion. After pretraining, we use the BP algorithm to finetune and update the whole network’s parameters in a topdown fashion. Results on the data of metro cards for a month period of Shanghai subway show that the proposed method outperforms  the wavelet neural network (waveletNN) and the autoregressive integrated moving average (ARIMA) in terms of prediction performance.