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Nowadays, Energy theft is widespread, how to improve the electricity theft identification of the user's power system has been concerned about the hot issues by the power company. With the popularity of smart meters in all regions, data mining and other large data analysis technology in the application of electricity data processing has been receiving increasing attention. Focused on the problem of anti-stealing electricity which power companies are concerned, and based on the study of the principle of support vector machine and the analysis of the characteristics of electricity data, the One-class SVM algorithm is introduced into the judgment of suspected energy theft, and an electricity theft identification model combining power fluctuation feature and One-class SVM is proposed. This paper first proposes an improved power data fluctuation coefficient to characterize the fluctuation of electricity, and then designs an electricity theft identification scheme based on One-class SVM. This method combines the fluctuation characteristics of electricity to select the load data samples, and constructs the detection model of energy theft based on the electricity data, then identifies whether there is electricity theft behavior. Experiments show that this method can improve the accuracy and efficiency of electricity theft identification and it has certain practicability.