Abstract: Accurate, rapid and efficient extraction and classification of typical loads of commercial users is an indispensable and important work for power grid companies to find out the electricity consumption behavior and demand rules of commercial users. In the context of big data, when the traditional clustering algorithm is used for commercial user load curves with high-dimensional collection and large difference in cluster results, there are some problems, such as difficulty in selecting truncation distance, unclear clustering effect and low efficiency of load pattern extraction. Therefore, an algorithm to improve local density measurement and selection of cluster center points is proposed. Firstly, the data is preprocessed to remove the load curve with high incompleteness, and then the PCA analysis method is used to reduce the dimension of the processed commercial user load curve, and then the improved SNN-DPC algorithm is used to calculate the distance matrix dist on the basis of constructing the sample point shared neighborhood set, replacing the distance matrix of the original algorithm as the input data. Then, based on the algorithm calculation of redefining SNN similarity, sample local density ρand distance from maximum density point δ, the inflection point is used to confirm the cluster center and complete the cluster analysis of the sampling curve. In short, the improved algorithm defines the similarity of the samples through the shared neighbors between the sample points, accurately analyzes some multi-dimensional heterogeneous load data, realizes the determination of the real clustering center point through the inflection point, and solves the problem of subjective will to select the clustering center, thus greatly improving the load clustering effect.The results show that:1) For the measured load data set of commercial users, this algorithm can accurately select the clustering center and has high operating efficiency.2) Compared with the traditional algorithm, the proposed load pattern recognition model based on the improved algorithm can better help power grid companies analyze the user’s electricity characteristics, and verify that the model can identify the typical load patterns of different commercial users more accurately.In summary, the strategy adopted in this paper exists in the real business user scenario.