The wavefront correction of adaptive optics is vulnerable to the interference of complex background, light intensity, noise signal and other problems, resulting in reduced correction effect. In order to solve these problems, a adaptive optics wavefront correction technique based on deep learning for airborne remote sensing communication system is proposed. The adaptive optics wavefront is detected by the phase difference method, and the noise is eliminated by the wavelet transform algorithm to avoid the interference of noise in the correction process. According to the prediction and self-learning ability of the deep neural network, a dynamic model network, a strategy network and a decision unit are constructed. By comparing with the correction threshold, the adaptive optics wavefront of the airborne remote sensing communication system is corrected. The experimental results show that the proposed method has a Stellerian ratio close to 1, and has a short correction time and good correction effect.