Abstract: This paper proposes a transfer learning method to recognize the orbital angular momentum beam to speed up the training speed of the orbital angular momentum beam recognition model based on deep learning. In order to simulate the atmospheric turbulence, we generate the atmospheric turbulence phase screen by the sub-harmonic method and build the simulated turbulence environment by loading the phase screen on the spatial light modulator. The orbital angular momentum beam recognition system based on transfer learning has achieved a recognition rate of more than 90% in both weak and medium turbulent environments. Compared with the traditional deep learning method in the aspects of model training speed and recognition rate, it is proved that the orbital angular momentum beam recognition method based on transfer learning can reduce the training time while maintaining a high recognition rate in the weak and medium turbulent environment.