The invention discloses an aircraft full-automatic aerodynamic optimization method based on reinforcement learning and transfer learning. The method is used for solving the problem that an existing pneumatic optimization method is prone to falling into local optimization or low in convergence speed, manual intervention is excluded in the final high-precision optimization stage through the optimization method, and the optimization efficiency is further improved. According to the technical scheme, firstly, a reinforcement learning environment based on semi-empirical estimation and high-precisionfluid simulation is established; a reinforcement learning neural network is constructed; a reward function is set, the global optimization capability of reinforcement learning is utilized; in the network training process, optimization experience is extracted from a semi-experience estimation method and stored in network parameters; then, another reinforcement learning neural network is constructed, migration learning is used for migrating the extracted optimization experience to the network, then the network is applied to aerodynamic optimization based on high-precision fluid simulation, andfinally, high-precision design parameters with excellent aerodynamic performance are obtained by training the network. Compared with a background technology method, the method has the advantages thatthe convergence speed is increased, the strong global optimization capability is realized, and the engineering value for high-precision pneumatic optimization is very high.