The vertical distribution of aerosols plays a critical role in improving the accuracy of aerosol retrieval in satellite remote sensing due to its complexity and spatiotemporal variability. This study investigated the vertical characteristics of aerosols using unsupervised clustering methods， based on CALIOP （Cloud-Aerosol Lidar with Orthogonal Polarization） Level 3 aerosol profile data from 2010 to 2020. Three clustering algorithms—Gaussian Mixture Model （GMM）， K-means， and spectral clustering—were evaluated using multiple performance metrics. The profiles of extinction coefficients were clustered into five representative types using the GMM algorithm： low-pollution composite type， high-pollution composite type， exponential decay type， low-pollution uniform type， and high-pollution oscillatory type. The seasonal and regional distributions of these profile types were further analyzed over the Tibetan Plateau， the Beijing-Tianjin-Hebei region， and the Yangtze River Delta. The results show that aerosol vertical profiles exhibit distinct seasonal and regional patterns. These findings provide a basis for improving aerosol profile parameterization and retrieval accuracy in remote sensing applications.