In　recent　years,　Graph　Neural　Networks　(GNNs)　have　achieved　unprecedented　success　in　handling　graph-structured　data,　thereby　driving　the　development　of　numerous　GNN-oriented　techniques　for　inductive　knowledge　graph　completion　(KGC).　A　key　limitation　of　existing　methods,　however,　is　their　dependence　on　pre-defined　aggregation　functions,　which　lack　the　adaptability　to　diverse　data,　resulting　in　suboptimal　performance　on　established　benchmarks.　Another　challenge　arises　from　the　exponential　increase　in　irrelated　entities　as　the　reasoning　path　lengthens,　introducing　unwarranted　noise　and　consequently　diminishing　the　model＇s　generalization　capabilities.　To　surmount　these　obstacles,　we　design　an　innovative　framework　that　synergizes　Multi-Level　Sampling　with　an　Adaptive　Aggregation　mechanism　(MLSAA).　Distinctively,　our　model　couples　GNNs　with　enhanced　set　transformers,　enabling　dynamic　selection　of　the　most　appropriate　aggregation　function　tailored　to　specific　datasets　and　tasks.　This　adaptability　significantly　boosts　both　the　model＇s　flexibility　and　its　expressive　capacity.　Additionally,　we　unveil　a　unique　sampling　strategy　designed　to　selectively　filter　irrelevant　entities,　while　retaining　potentially　beneficial　targets　throughout　the　reasoning　process.　We　undertake　an　exhaustive　evaluation　of　our　novel　inductive　KGC　method　across　three　pivotal　benchmark　datasets　and　the　experimental　results　corroborate　the　efficacy　of　MLSAA.