The classification of biological and abiotic targets in the air is an important part of bird strike control in the airport. Target classification based on trajectory information has the advantages of easy access to trajectory information and high degree of discrimination of some features, but improper feature selection will result in large classification errors of close-range trajectory samples. Aiming at this problem, an aerial target classification algorithm based on double-layer feature selection is proposed. First, fully feature extraction is performed on the three-dimensional trajectory data of dynamic targets to expand the range of feature selection. Second, the feature subset is selected through the designed two-layer feature selection algorithm, which reduces the computational complexity of the algorithm and improves the classification precision. Finally, online sequential extreme learning machine (OSELM) is used to realize the real-time classification of aerial biological and abiotic targets. Experimental results show that the proposed algorithm takes into account the accuracy and speed of classification, the classification accuracy reaches 99.7%, and the average classification time is only 1.26 ms, which meets the needs of real-time monitoring and early warning. The proposed algorithm provides a potential solution for real-time classification of air targets under airport conditions.