Knowledge　graph　(KG)　is　evolving　rapidly　and　play　an　important　role　in　many　applications.　Recently,　the　few-shot　knowledge　graph　completion　(FKGC)　task,　which　involves　predicting　missing　information　based　on　a　limited　number　of　known　facts,　has　garnered　growing　interest　from　both　practitioners　and　researchers.　However,　these　methods　often　fail　to　fully　utilize　neighboring　information　and　overlook　the　semantic　distance　between　similar　entities.　To　address　these　problems,　we　introduce　a　multi-hop　neighbor　aggregator　based　on　CNN　which　designs　to　make　comprehensive　use　of　neighbor　information.　Additionally,　we　employ　contrastive　learning　to　reduce　the　semantic　distance　between　similar　entities.　Compared　to　the　leading　baseline　GANA,　our　model　shows　an　improvement　of　0.4%　and　0.4%　on　NELL　in　terms　of　Hits@1　and　Hits@5,　respectively,　and　5%,　1.4%,　and　0.4%　on　Wiki　in　terms　of　Hits@1,　Hits@5,　and　Hits@10.　Extensive　experiments　demonstrate　that　our　method　performs　exceptionally　well　on　two　public　datasets.　©　2024　SPIE.