The path planning of UAV area coverage in unknown environment is studied based on deep reinforcement learning method. By building a grid environment model, randomly deploying UAV and no-fly zone in the environment, and using a double deep Q-network(DDQN) to train the coverage strategy of UAV, a set of UAV coverage path planning framework base on DDQN is obtained. The simulation experiment shows that the designed UAV unknown area coverage path planning framework can achieve full coverage in the environment without no fly zone, and can also better complete the area coverage task in the environment with an unknown number of no fly zones. Compared with DQN method, its average coverage rate can be 2% higher under the same training conditions and training rounds, higher than Q Learning method and Sarsa method in the environment without no fly zone.