Abstract: To address the issues of inefficient training, slow convergence, and poor path feasibility encountered by deep reinforcement learning algorithms in solving dynamic path planning for low-altitude aircraft, a TCP-DQN (target-guided curriculum learning and prioritized replay deep Q-network) based dynamic path planning algorithm is proposed. Firstly, a curriculum learning mechanism is introduced into the framework of reinforcement learning algorithms, where target-guided maneuver strategies are set to improve the training speed of the algorithm while optimizing the feasibility of the planned paths. Secondly, a combined reward function for training is constructed to resolve the sparsity problem of DQN reward values, and obstacle avoidance experiences of low-altitude aircraft are prioritized for replay to enhance the learning performance of the algorithm. Finally, simulation results of the TCP-DQN algorithm for path planning in 3D low-altitude dynamic environment are presented. The simulation results demonstrate that the algorithm can quickly plan the safe and efficient paths for low-altitude aircraft in dynamic and unknown threat environments.