In order to solve the problem of abnormal load identification based on smart meter sampling data in the context of unknown household appliance load,this paper proposes a user abnormal load identification method based on edge computing based on the charging load of electric vehicles.Firstly,the Boruta-SHAP algorithm was used to sort and screen 14 features of the non-intrusive load data,and the subset of features with the best identification effect under the second-level load data was obtained.Then,the v-non parallel support vector machine(v-NPSVM)model was used to train the abnormal load recognition model.Finally,the algorithm is deployed on the edge computing platform combined with edge computing technology to realize the identification of typical electric vehicle charging loads.The experimental results show that the correct identification rate of abnormal load identification after frequency reduction is still more than 90%,which proves that the proposed method has good applicability and accuracy in the context of unknown household appliance load.