Load clustering can not only provide high-quality data for fine load forecasting, but also help carry out user behavior analysis according to the law of electricity consumption. In order to meet the challenge of processing massive data, a dimension reduction and improved K-means clustering algorithm based on daily load indicators is proposed in this paper. Firstly, the original high-dimensional load data is converted into low-dimensional data by establishing a daily load indicator. Then, the distributed K-means algorithm improved by the entropy weight method is used to cluster the low-dimensional data in order to discover hidden typical load types. Finally, combing with the example, the electricity consumption law is analyzed according to the obtained typical load, and it is matched with the actual user type, and the four typical electricity consumption laws are summarized.