Graphs　have　become　a　widely-used　tool　to　model　data　with　relationships　in　real　life　for　a　long　time.　To　discover　the　important　contents　in　the　graph,　many　graph　neural　networks　(GNNs)　have　been　come　up　with.　Nevertheless,　these　models　tend　to　adopt　ReLU　as　their　activation　function　for　its　nonlinearity,　effectiveness,　and　efficiency.　Owing　to　its　own　deficiency,　it　would　cause　many　generated　feature　elements　to　be　zero,　which　would　miss　part　significant　features.　To　overcome　this　problem,　an　adaptive　weight　vector　to　tune　the　features　was　provided.　By　limiting　the　elements　of　the　weight　vector,　it　can　be　a　better　substitute　for　ReLU.　Besides,　such　a　weight　vector　adaptively　measures　the　importance　of　each　feature　element　to　work　as　a　feature　selection　operator.　To　show　the　function　of　the　weight　vector,　we　examine　a　GCN　with　the　weight　vecotor　in　node　classification,　and　it　exhibits　overall　improvements　over　three　well-known　citation　networks.　©　2023　IEEE.