Abstract: Aiming at the bias problem of electricity theft detection caused by the imbalance of power user classes, a class imbalanced theft detection model based on resampling and hybrid ensemble learning is proposed. Firstly, the optimal number of sampling subsets is determined based on the Easy-ensemble hybrid ensemble learning framework. Then, through the resampling adaptive strategy, that is, according to the imbalance of the user's electricity data set and the optimal number of sampling subsets, the resampling method of the detection model is determined, achieving the balance of the electrical data. Finally, according to the hybrid ensemble mode, i.e., first serially ensembling the data to reduce deviation and then parallelly ensembling them to reduce variance, the resampled balanced sample is detected for power theft. The comparative analysis of the study example shows that the proposed detection model effectively solves the bias problem of the traditional ensemble algorithm in the detection of unbalanced electricity theft through resampling and hybrid ensembling. The influence of the imbalance of the electricity consumption data on the ensemble results is reduced, and the imbalanced electricity theft detection effect of the user category is improved, which shows that the proposed model performs very well in accuracy, F1 value and G mean under various imbalances.