Channel equalization is used to suppress the inter symbol interference caused by atmospheric turbulence in the broadband optical wireless coherent communication system. In this article, intermediate frequency signals in turbulent environment are used as training samples. Back propagation (BP) neural network and long short-term memory (LSTM) neural network are utilized for training. The trained stable network model is used as a channel equalizer, and the output intermediate frequency signal by equalizer is used as the evaluation index of system performance and compared with the adaptive optics wavefront distortion correction algorithm. The simulation results show that by using BP neural network channel equalization technology, LSTM neural network channel equalization technology, and wavefront distortion correction technology, the peak values of intermediate frequency signal histogram are located at 0. 49, 0. 38, and 0. 38 V, and the corresponding system bit error rate is 3. 79×10 -5 、1. 64×10 -4 and 8. 48×10 -2 , respectively. Compared with wavefront distortion correction technology, intelligent channel equalization technology has significantly improved on the random fluctuation of intermediate frequency signal amplitude and bit error rate.