The invention provides an improved local anomaly factor algorithm-based topology identification method. The method comprises the steps of firstly, based on the statistical theory, acquiring the operating state change information of a to-be-predicted device object, such as a switch, a disconnecting link or the like; secondly, according to the acquired data, modeling a to-be-identified data object, and respectively establishing an object set for each device object, wherein the object set represents the operating state change condition of the device object within a certain period of time; thirdly, based on the grid reduction theory in the GDLOF algorithm, reducing data objects in the object set to reduce identification objects and improve the efficiency of the algorithm; fourthly, for non-excluded data objects, subjecting each attribute of each object to weighted treatment by adopting a relative entropy in considering different influences of the telemetry and remote signaling information on topology error identification. In this way, the reliability and the execution efficiency of the algorithm are improved, and the identification topology error of a local anomaly factor is finally confirmed. According to the technical scheme of the invention, the density-based anomaly detection algorithm is applied to the topology error identification of the power grid, so that the application field of the anomaly detection algorithm is expanded. Meanwhile, the topology error problem of the power grid and the identification problem of telemetry bad data in the prior art are solved at the same time.