There is a deficiency in the single classification method in traditional electricity thief detection. Thus a method based on AdaBoost ensemble learning is proposed. First, the training set is used to compare the decision tree, error backpropagation network, support vector machine and k-nearest neighbors, and the decision tree is adopted as the weak learner of the AdaBoost algorithm. Secondly, the learning rate and the number of weak learners of AdaBoost ensemble learning are determined by plotting the error rate curves under different learning rates. Finally, the proposed method is tested and evaluated on the Irish smart meter dataset. It is compared with the single strong learning algorithms, such as decision tree, error backpropagation network, support vector machine, k-nearest neighbors. The results show that electricity theft detection based on AdaBoost ensemble learning is the best among the indicators of accuracy, true positive rate and false positive rate. The sensitivity analysis shows the validity of the electricity theft detection method based on AdaBoost ensemble learning. This work is supported by National Natural Science Foundation of China (No. 51607104) and 2019 Science and Technology Project of State Grid Hubei Electric Power Company (No. 5215K018006B).