The clock?inaccuracy at the load measuring point on the 10 kV line leads to an abnormal line loss rate, while the existing manual methods have the problems of low efficiency and low intelligence. Therefore, based on the fluctuation characteristics of line loss rate curve, a new method for identifying the load types of clock?inaccuracy metering points is proposed to fit the mapping relationship between load type and the clock?inaccuracy line loss rate by Bayesian network (BN). In order to solve the problem of lack of clock?inaccuracy samples, the metering clock deviation modules are respectively set for the load metering points to generate a sample set of clock?inaccuracy line loss rate in the simulation model based on the actual operation data of the line. The fuzzy C?means clustering is then introduced to classify the load according to the shape similarity of the load curve, and the data dimensionality reduction is realized in scenarios with heavy load. Relying on research data from the synchronous line loss management system, the calculation example verifies the feasibility and accuracy of the proposed method. It is shown that the method can realize load type identification of clock?inaccuracy, and provide a reference for quickly locating the abnormal energy meters.