Adaptive beamforming technology is widely used in sidelobe anti-interference in the radar field. When the amount of echo data increases, the traditional beamforming algorithm cannot perform fast processing, and the deep neural network model can quickly perform beamforming through data pre-training. Therefore, this paper designs a deep neural network according to the beamforming principle. The deep neural network is compressed by means of knowledge distillation, so that the compressed model has both good generalization performance and faster calculation speed. The simulation results show that compared with the traditional LMS algorithm, the computational speed of the adaptive beamforming algorithm for deep neural networks without model compression is improved by about 7 times and the computational speed of the adaptive beamforming algorithm based on model compression is improved by about 20 times in the experimental environment.