Social recommendation introduces users?? social information based on traditional recommendation to generate better recommendation results. As social recommendation involves not only the user?? s information but also the user?? s social relationship information privacy protection for users becomes more important. However most current social recommendation methods focus only on improving recommendation accuracy and overlook the issue of privacy protection for users?? personal information. Therefore a privacy protection method for the social recommendation based on community division SRCD was proposed to address the privacy protection issues of users?? rating data and social relationship data in the social recommendation. Methods First considering the impact of rating value distribution on user similarity and combined with the social relationships between users communities in the social network were divided and the average ratings of users for items they have viewed in each community were calculated. Then based on the results of community division similar communities to the community where the target users belong were identified. This allowed for the construction of a community-item rating average matrix. Additionally for sparse rating average matrices in practical scenarios median imputation was used to fill in missing elements in the matrix. Finally matrix decomposition results were used to predict user ratings for items thus evaluating the algorithm ?? s performance. Results Through simulation experiments it was verified that the proposed method not only provides a guarantee for privacy protection but also has a similar prediction accuracy in recommendation accuracy compared with existing social recommendation algorithms. Conclusion The proposed method not only protects users ?? privacy information to a certain extent but also provides satisfactory recommendation results for users.