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Aiming at the problem of target autonomous tracking in the process of air docking, an end-to-end air target autonomous tracking method based on deep reinforcement learning is proposed. In this method, the near end strategy optimization algorithm is adopted. The strategy network and value network share the first two network parameters. The image captured by UAV is used as the input of convolution neural network. The motor speed of rotor UAV is controlled by strategy network to achieve end-to-end autonomous target tracking. At the same time, shaping meth-od is used to accelerate the agent training. The simulation environment is built by pybullet and the training verification is carried out. The experimental results show that the method can achieve the set target tracking requirements and has good robustness.