The invention relates to an electric power data parallelization anomaly detection method based on a MapReduce. The method comprises the following steps that first, electric power data anomaly characteristics are defined by a grid company according to the operation feature of an acquisition system and expert historical experience before, and accordingly an anomaly detection algorithm is established; second, a cluster calculation model is established; third, when processing data arrive, a Master node of the cluster calculation model starts a data anomaly detection task and distributes the calculation task to all Slave nodes in a relatively even mode; fourth, all the Slave nodes conduct anomaly detection calculation according to the task distributed by the Master node and the anomaly detection algorithm established in the step S01; fifth, the Master node outputs abnormal data according to the calculation of all the Slave nodes. By means of the electric power data parallelization anomaly detection method based on the MapReduce, the problem that the calculating and analyzing efficiency is low under the background of a huge quantity of electric power data in the prior art can be solved, and a forceful guarantee is provided for improving power grid efficiency and reducing loss.