This paper addresses the difficulty of the accurately detecting electricity theft in complex grid environment and proposes a multi?category electricity theft detection method based on CNN?LSTM hybrid model. Firstly, the excellent feature abstraction ability of convolutional neural networks (CNN) is utilized to extract the non?periodic local features of one?dimensional electricity consumption data. Then, the long short?term memory (LSTM) is adopted to capture the correlation between daily power consumption data and extract periodic power consumption features to establish feature fusion layer network. After that, the feature vectors extracted by CNN and LSTM are horizontally splicing to obtain a new fusion vector. Based on this, the accurate detection of multiple types of electric theft behavior are realized. Experimental results show that the proposed method can accurately identify multiple types of electric theft behavior, and the detection results are more comprehensive and accurate than the existing detection methods.