The invention discloses a deep learning method for flow abnormity detection of an intelligent power grid server. According to the method, on the basis of the high-dimensional time sequence data information for monitoring the flow of a power grid server, an Encoder-Dector deep neural network based on two layers of LSTM neurons is utilized to construct the flow normal state estimation, and based onthe estimated normal flow behavior, the distribution condition of the residual error between the normal flow behavior and the measured value is analyzed, and finally a confidence interval is given asa control limit for judging the abnormal behavior, thereby realizing the detection of the abnormal flow behavior of the smart grid server. According to the method, the nonlinear relation between variables can be dynamically approximated in a self-adaptive mode, the manual intervention is not needed for selecting variable characteristics, the data dimension reduction is not needed, the certain universality is achieved, and the method has very important scientific significance and application value for preventing severe faults.