In order to solve the problem of abnormal data identification for key indicators with the deepening and development of power enterprise reform, this paper proposes a method of dangerous trend identification model based on a statistical analysis of abnormal power generation behavior data. The method includes a data access scheme, feature extraction scheme, and anomaly detection algorithm. The experimental results show that the proportion of users whose electricity consumption behavior conforms to the peak period electricity consumption > normal period electricity consumption > valley period electricity consumption exceeds 90%. More than 85% of users’ electricity consumption behavior is in line with the proportion of electricity consumption that is less than 0.25 in millet hours. The proportion of users whose fluctuation coefficient of electricity consumption in the valley period is less than 1 exceeds 85%, and 99.9% of users’ fluctuation coefficient of electricity consumption in the valley period is less than 5, which proves that abnormal power generation behavior data and abnormal power consumption data can bring early warning to some dangerous power consumption behaviors. The statistical analysis model of abnormal power generation behavior data can play a positive role in the identification of dangerous trends.