To better detect the effect of printed images and improve the production efficiency, a meta-learning based blind image quality assessment model for complex distortion and content changes of images is proposed in this paper. First, in the meta-training part, the shared distortion prior knowledge of multiple distortion data sets is obtained through the ResNet50 network; then, in the meta-testing part, the multi-level features of ResNet50 are merged to achieve a complete description of the image local and global distortion; finally, through the feature dimension reduction and fusion to obtain the multi-level feature weights, a network model for image quality assessment is established. A large number of experimental results have proved that the prediction performance and generalization performance of the proposed model are better than other algorithms on the authentic distortion dataset (LIVEC) with SRCC of 0.87 and the synthetic distortion dataset (LIVE) with SRCC of 0.97. The proposed meta-learning based blind image quality assessment method can accurately predict the quality scores of different types of images, which can provide certain guidance for the quality evaluation of printing images and the control of printing production.