Adaptive Optics(AO) imaging system is affected by residual atmospheric turbulence, closed-loop tracking error and photoelectric detection noise, and imaging results are blurred to varying degrees, which is not conducive to the later image screening and post-processing, thus, it is necessary to evaluate the image quality. Traditional image quality assessment methods are not reliable for no-reference AO image quality assessment, and even the assessment results deviate from the actual situation. Aming at the above problems, according to the imaging process of AO system, an AO degradation image dataset with image quality labels is generated. On this basis, a neural network model for assessing the AO image quality is trained by using a deep neural network with ResNet as the backbone, and the best Spearmans Rank Order Correlation Coefficient (SROCC) on the dataset is 0.994. The experimental results show that this method comprehensively considers various degradation factors in the process of AO imaging, a no-reference AO image quality assessment model is obtained by training deep neural network, and the assessment accuracy is better than that of other traditional image quality assessement algorithms.