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At this stage, radar target detection and recognition mainly rely on artificial algorithms to extract the target's characteristics. The difficulty lies in the weak environmental adaptability, and it is difficult to effectively detect the target under the background of high-intensity clutter. In response to the above problems, combined with the powerful learning and representation capabilities of deep learning in image recognition and other fields, a radar target recognition method based on stacked bidirectional long short-term memory network is proposed. The network model constructs a data set with radar Doppler-dimensional echo data, uses bidirectional LSTM to extract the forward and reverse information of radar echo data in the time series, and iteratively trains the neural network parameters through the RMSProp optimization algorithm. Effective recognition of low-altitude and slow-speed small targets such as unmanned aerial vehicle. Experimental results show that the radar target recognition based on stacked bidirectional LSTM is better than the traditional SVM classification algorithm and convolutional neural network classification algorithm.