With the rapid development of Internet,it is difficult for users to find the content they are interested in from massive network data,while the recommendation system can solve this problem.Traditional recommendation systems only rely on user’s historical behavior data for recommendation,which has the problems of data sparsity and cold start.The integration of social network information into the recommendation system has been proven to effectively solve the problems of the traditional recommendation system and improve the quality of recommendation system.However,most recommendation systems based on social networks only focus on the one-way trust relationships between users,and ignore the influence of the trusted relationship and the item’s own factors on recommendation results.Therefore,a social trust recommendation algorithm,called SocialIS,which combines item similarity,is proposed.The influence of neighbor users on user when the user is truster and trustee is considered by SocialIS,and the Node2vec algorithm is used to train the item similarity vector containing the user’s preference,and then the graph neural network is used to learn the feature vector of the user and the item to predict the score.A large number of experiments are conducted on the Epinions and Ciao data sets,and the performance of the proposed algorithm is measured by error-based indicators (MAE and RMSE),and compared with other algorithms to verify its performance.Experimental results show that compared with other algorithms,the proposed algorithm had smaller scoring prediction error and better recommendation effect.