Contrastive　learning　is　a　commonly　used　framework　in　the　field　of　graph　self-supervised　learning,　where　models　are　trained　by　bringing　positive　samples　closer　together　and　pushing　negative　samples　apart.　Most　existing　graph　contrastive　learning　models　divide　all　nodes　into　positive　and　negative　samples,　which　leads　to　the　selection　of　some　meaningless　samples　and　reduces　the　model＇s　performance.　Additionally,　there　is　a　significant　disparity　in　the　ratio　between　positive　and　negative　samples,　with　an　excessive　number　of　negative　samples　introducing　noise.　Therefore,　we　propose　a　novel　dynamic　sampling　strategy　that　selects　more　meaningful　samples　from　the　perspectives　of　structure　and　features　and　we　incorporate　an　iteration-based　sample　selection　process　into　the　model　training　to　enhance　its　performance.　Furthermore,　we　introduce　a　curriculum　learning　training　method　based　on　the　principle　of　starting　from　easy　to　difficult.　Sample　training　for　each　iteration　is　treated　as　a　task,　enabling　the　rapid　capture　of　relevant　and　meaningful　sample　information.　Extensive　experiments　have　been　conducted　to　validate　the　superior　performance　of　our　model　across　nine　real-world　datasets.