The invention relates to an unmanned aerial vehicle maneuvering target tracking method based on DDPG transfer learning, and the unmanned aerial vehicle maneuvering target tracking method carrying outtask decomposition, initializes an environment state, neural network parameters and other hyper-parameters, and carries out the training of a neural network. At the beginning of each round, the unmanned aerial vehicle executes an action to change the speed and the course angle, to obtain a new state, stores the experience of each round in an experience pool to serve as a learning sample, and continuously iterates and updates parameters of the neural network. And when the training is completed, the neural network parameters trained by the sub-tasks are stored, and are migrated to the unmanned aerial vehicle maneuvering target tracking network in the next task scene until the final task is completed.