The　phenomenon　that　AI　researchers　tend　to　transform　some　certain　data　into　the　form　of　graphs　is　prevailing.　Usually,　these　graph-like　data　will　be　inputted　into　some　certain　artificial　neutral　networks　which　are　dramatically　disparate　with　the　conventional　CNN.　The　purpose　of　the　algorithm　employed　the　GNN　is　to　extract　much　more　detailed　features　that　can　be　stored　easily　in　the　graph.　However,　these　detailed　features　are　much　more　difficult　to　extract　in　the　raw　data　accumulated　in　the　database,　which　requires　the　experiment　to　transfer　the　common　database　into　the　whole　graph　ahead　of　schedule.　The　dataset　used　in　this　paper,　Cora,　is　commonly　used　in　some　papers　whose　targets　aimed　at　semantic　segmentation,　while　disparate　with　this　paper　as　well.　The　result　of　this　experiment　has　achieved　to　nearly　100%　accuracy　accompanied　with　those　preprocessed　data.　Furthermore,　this　paper　also　attaches　much　focus　on　the　effects　of　preprocessing　operation　which　can　be　reflected　on　the　differences　of　accuracy.　Only　by　preprocessing　operation　can　this　paper　achieve　better　results　accompanied　with　higher　accuracy　when　compared　with　other　experiments.　©　2020　IEEE.