In　Graph　Neural　Networks　(GNNs),　a　common　feature　across　many　datasets　is　the　Power-law　Distribution　of　node　degrees,　where　most　nodes　exhibit　few　connections,　contrasting　with　a　small　fraction　that　possesses　a　high　number　of　links.　This　difference　often　introduces　training　instability　and　compromises　performance　on　tasks　like　node　classification,　particularly　for　low-degree　nodes.　To　tackle　these　challenges,　we　introduce　RUNCL:　:　R　elationship　U　pdating　N　etwork　with　C　ontrastive　L　earning,　a　novel　model　designed　to　ensure　that　the　model　learns　more　accurate　node　features,　especially　the　features　of　low-degree　nodes.　Specifically,　RUNCL　comprises　a　graph　generation　module　that　generates　different　neighborhood　information　graphs　based　on　the　node　feature　graphs.　The　optimal　graph　selection　module　selects　the　neighborhood　information　graph　that　best　reflects　the　relationship　between　nodes　and　a　contrastive　learning　module　to　learn　more　accurate　node　embeddings　by　contrasting　positive　and　negative　samples.　We　evaluate　the　performance　of　RUNCL　on　six　datasets,　and　the　experimental　results　demonstrate　its　effectiveness.　The　model　exhibited　an　improvement　of　2.5%　in　the　testset.　Moreover,　the　model＇s　performance　boosted　to　6%　when　the　testset　only　included　low-degree　nodes.　The　implementation　and　data　are　made　available　at　https://github.com/pengyu-zhang/RUNCLRelationship-Updating-Network-with-Contrastive-Learning.