In the context of big data, the informatization of China’s power industry is becoming more important, especially the analysis of power consumption data with computer technology. For the analysis of abnormal user power consumption, traditional methods are time-consuming and labor-intensive. This requires the introduction of machine learning related methods to automatically identify anomaly information. At this stage, the analysis of abnormal power consumption is mainly based on traditional anomaly detection algorithms or deep neural networks. Anomaly detection algorithms have insufficient accuracy and calculations with deep neural networks are quite slow. In response to the current shortcomings, this study adopts an anomaly detection model of user power consumption based on sampling technology and LightGBM. The detection of abnormal power consumption is regarded as a classification problem, and the popular classification model LightGBM is applied to training. The detection accuracy is improved while fast speed is maintained.