In view of the trend of weak subsynchronous oscillation signal in the normal operation of power system, poor noise resistance and low reliability of identification results, a identification method of subsynchronous oscillation mode parameter based on deep residual network is proposed.A deep residual network model composed of convolutional layer, several residual layer and fully connected layer is established; the model training data set is generated according to the characteristics of SSO signal, all using simulation data; the parameter adjusted and optimized model can realize the blind identification of low SSO signal mode parameters measured in the field.Using ideal signal, noise simulation signal and field measured data three schemes of the model performance verification, the results show that the algorithm can effectively identify the weak SSO frequency and damping and other key parameters, compared with convolutional neural network (CNN) and random subspace (SSI) algorithm, higher accuracy, small noise interference, has the characteristics of blind identification, can be used for power system secondary synchronous oscillation risk warning.