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With the continuous development of unmanned mobile platforms, it has become particularly important to endow them with efficient autonomous navigation capabilities. In response to the low training efficiency, poor generalization ability, and universality of the commonly used end-to-end reinforcement learning methods for autonomous navigation of UAV, a brain-inspired navigation model is introduced. Based on the long short-term memory (LSTM) neural network, a brain-inspired cell navigation model is constructed, which integrates the self-motion information of the UAV intelligent agent to encode grid cells and head direction cells, and further supplements this information as the state of the deep reinforcement learning algorithm D3QN. The experiments in AirSim simulation environment show that the introduction of the brain-inspired navigation model can effectively improve the training ability of the algorithm and the navigation performance of the UAV intelligent agent. Compared with the original D3QN algorithm, it can still find new target points when the environmental target changes, effectively improving the generalization ability of the algorithm.