In this study, we employ our developed instrument to obtain high-throughput multi-angle single-particle polarization scattering signals. Based on experimental results of a variety of samples with different chemical composition, particle size, morphology, and microstructure, we trained a deep convolutional network to identify the polarization signal characteristics during aerosol scattering processes, and then investigate the feasibility of multi-dimensional polarization characterization applied in the online and real-time fine and accurate aerosol recognition. Our model shows a high classification accuracy rate (>98%) and can achieve aerosol recognition at a very low proportion (<0.1%), and shows well generalization ability in the test set and the sample types not included in the training set. The above results indicate that that the time series pulses from multi-angle polarization scattering contain enough information related with microscopic characteristics of an individual particle, and the deep learning model shows its capability to extract features from these synchronous multi-dimensional polarization signals. Our investigations confirm a good prospect of aerosol attribute retrieval and identifying and classifying individual aerosols one by one by the combination of multi-dimensional polarization scattering indexes with deep learning method.