To　avoid　data　overload,　recommendation　systems　have　been　created.　Due　to　the　difficulty　of　data　collection,　the　recommendation　system　faces　a　cold　start　and　needs　to　introduce　auxiliary　information.　In　this　paper,　we　use　social　recommendation　to　solve　the　cold　start,　and　we　adopt　a　graph　convolutional　neural　network　to　aggregate　high-order　neighbors　and　sample　the　neighbors　for　the　auxiliary　recommendation　system.　Ultimately　our　model　achieves　impressive　results　on　classical　datasets.　Compared　to　the　baselines,　we　achieved　a　higher　accuracy　rate.　©　2022　SPIE.