The traditional electrocardiogram ( ECG) signal recognition algorithms rely on ECG experts toparticipate in feature recognition，which is time-consuming and laborious with high diagnostic cost. Complex anddiverse ECG signal patterns result in low recognition accuracy and poor adaptability. To solve the above problems，the stack Sparse Autoencoder was combined with the Softmax classifier to form a Deep stack Network to realizeautomatic recognition of ECG signals. The feature extraction of ECG signals was completed by stacking three sparseautoencoders，and the high-dimensional features of ECG signals were depicted layer by layer，and the ECG signalswere identified by Softmax classifier. Detailed assessment of the model characteristic of Deep stacked Network，determine the super parameter of the network model，sample training set and test set samples from MIT /BIHdatabase. The experimental results show that the total recognition rate of the proposed method is 99. 69%，whichverifies the effectiveness of the proposed method.