Abstract: In order to solve the problems of low precision and redundant feature parameters in the process of aerosol classification, several significant aerosol feature parameters were extracted from the OMI (Ozone Monitoring Instrument) remote sensing products and Random Forest (RF) algorithm was used for aerosol type classification and verification. Aerosols of Guangdong Province in 2014 were divided into three types: desert dust (DST), carbonaceous aerosols associated with biomass burning (CRB) and sulfate-based urban-industrial aerosols (SLF). The Random Forest classification results and the importance of feature parameters were analyzed. The spatial distribution of cla-ssification results were compared to that of OMI aerosol type products. The following results are obtained. First, with the RF algorithm, a total precision of over 97% can be reached with a few training samples. Second, calculating the importance of different aerosol feature parameters in RF shows that the most important feature parameters are angstrom exponent, UVAI, RI₃₈₈, RI₃₅₄, SSA₅₀₀ and AAOD₅₀₀ in turn, indicating that size distribution and absorption ability of aerosols play the key roles. Third, the spatial distribution of three aerosol types shows that sulfate-based urban-industrial aerosols are dominant in the Pearl River Delta. Proportion of biomass burning and desert dust aerosols are highest in the Pearl River Delta and lowest in the eastern and northern Guangdong.