Aiming at the problem that traditional power consumption behavior analysis methods are difficult to cope with massive data processing demands, a power consumption behavior detection method based on power IOT data is proposed. First, the business and data features are fused to construct an electricity consumption behavior feature model, and a feature importance assessment method with maximum information coefficient and variance fusion is proposed. Second, a DBSCAN (K-DBSCAN) power usage behavior classification algorithm combined with K-means assisted parameterization is proposed, while the feature set is determined by recursive feature elimination. Finally, it predicts the electricity load of each type of user in combination with the environmental factors on the user's side, and analyzes the electricity behavior of each type of user in depth based on the upper and lower limits of the predicted load. The experimental results show that the K-DBSCAN algorithm achieves a significant improvement of 3.32% in the clustering effect of user behavior compared with the baseline algorithm.