LIDAR plays significant roles in monitoring the vertical distribution characteristics of clouds and aerosols and studying their impacts on the global climate change. For the space-born LIDAR, discrimination between clouds and aerosol is the first step of cloud/aerosol vertically optical property retrieve, and to a great extent, the retrieval precision depends on the accuracy of cloud and aerosol classification algorithm. Based on the optical and geographic characteristics of aerosols and clouds observed by LIDAR, in this study, the CALIOP aerosol and cloud products over China in the year of 2016 were trained as the sample sets. An effective cloud/aerosol classification algorithm was developed by combining the support vector machines (SVM) and decision tree methods. Our algorithm includes 3 parts: cloud and aerosol discrimination, ice-water cloud classification and aerosol subtype classification. (1) The cloud and aerosol were discriminated by the classification confidence functionsof 5-D probability density function (PDF) with parameters of γ532, χ, δ, Z and lat. (2) Randomly oriented ice (ROI) and water cloud were classified based onthe SVM. And by constructing the PDFs with γ532, χ, δ, Z and T, feature layers misclassified by SVM were corrected, and a small portion of the horizontally oriented ice (HOI) clouds were removed from the water clouds. (3) Based on the optical and geographic characteristics of aerosol subtypes, decision tree classification was used for the determination of aerosol subtypes. Our retrieval results showed a good agreement with the CALIOP VFM products. For the cloud and aerosol discrimination results, the consistency ratios between our retrieves and VFM products for aerosol and cloud are up to 98.51% and 88.43%, respectively. And the consistency ratios in the day are higher than those at night. For the cloud phase retrieval results, water clouds can be well separated, and the consistency ratio of water cloud between our retrieves and VFM products is as high as 93.44%. The consistency ratio of HOI is low due largely to the confusion between HOI and ROI. For the aerosol subtype classification, most aerosol subtypes could be well recognized by our algorithm. However, the consistency ratios of the mixed subtypes (e. g. polluted continental and polluted dust) between retrieval results and VFM products are relatively lower. Moreover, the cloud/aerosol, cloud phase and aerosol subtype classifications were also compared with the VFM products under three typical air conditions, i. e. haze, dust and clean. Under the haze condition, our results for most of the smoke aerosols agree quite well with the corresponding results from VFM. Under the duststorm condition, our algorithm can effectively discriminate the most of dust and polluted dust aerosols. For the clear day, our results for the few existing cloud and aerosol layers are quite consistent with the VFM results. This paper is an important improvement of the cloud and aerosol classification algorithms, which can simplify the processing and improve efficiency with satis factory accuracy. In the future work, we will build day/night and seasonal training sample sets, and consider more ice cloud phases and aerosol properties in the cloud/aerosol classification retrieval algorithm.