Wide-Angle Synthetic Aperture Radar (WA-SAR) has a broader coverage of angles, and Wide-Angle Scattering Centers (WA-SCs) derived from it encompass richer electromagnetic scattering characteristics of target objects, which is important for the following analysis. To address the high-dimensional and complex nature of WA-SCs data and extract targets’ features, Density Peak Clustering (DPC) was applied to WA-SCs. Based on the SLICY model dataset, from three aspects of clustering internal evaluation, clustering visualization and automation of algorithm, DPC is compared with three classical KMeans, DBSCAN and MeanShift algorithms. The results show that DPC has advantages of high degree of automation, high dimensional data adaptability, high accuracy of clustering and so on, which is expected to provide technical support for target modeling and target recognition through WA-SCs.