Abstract: Traditional cross-rating collaborative filtering paradigm ignores the influence of rating density in the target domain on the accuracy of user and item latent vectors, resulting in less accurate rating prediction in regions with sparse ratings. To overcome the influence of regional rating density on rating prediction, based on the thought of transfer learning, a cross-region and cross-rating collaborative filtering recommendation algorithm (CRCRCF) is proposed. Compared with the traditional cross-rating collaborative filtering paradigm, CRCRCF algorithm can effectively exploit not only the important knowledge from the auxiliary domain, but also the important knowledge from the rating-dense regions in the target domain, which can further improve the rating prediction accuracy of the whole target domain, especially the rating-sparse regions. Firstly, for users and items, active users and inactive users, popular items and unpopular items are divided respectively. Graph convolution matrix complementation algorithm is used to extract the latent vectors of active users and popular items in the target domain and all users and items in the auxiliary domain. Secondly, for users and items in rating-dense regions, deep regression models based on self-taught learning are constructed to learn the mapping relationships between latent vectors in the target domain and in the auxiliary domain, respectively. Then the mapping relationships are generalized to the whole target domain, and the relatively accurate latent vectors of inactive users and unpopular items in the auxiliary domain are used to derive their latent vectors in the target domain, which achieves the cross-region mapping relationships transfer and cross-rating latent vector information transfer successively. Finally, the restricted graph convolutional matrix completion model is proposed with the obtained latent vectors of inactive users and non-popular items in the target domain as constraints, and the corresponding recommendation results are given. The simulation experiments on MovieLens and Netflix datasets show that the CRCRCF algorithm has obvious advantages over other state-of-the-art algorithms.