Recently,　in　the　field　of　crystal　property　prediction,　the　graph　neural　network　(GNN)　model　has　made　rapid　progress.　The　GNN　model　can　effectively　capture　high-dimensional　crystal　features　from　the　crystal　structure,　thereby　achieving　optimal　performance　in　property　prediction.　However,　the　existing　GNN　model　faces　limitations　in　handling　the　hidden　layer　after　the　pooling　layer,　which　restricts　the　training　performance　of　the　model.　In　the　present　research,　we　propose　a　novel　GNN　model　called　the　batch　normalization　multilayer　perceptron　crystal　distance　graph　neural　network　(BNM-CDGNN).　BNM-CDGNN　encodes　the　crystal＇s　geometry　structure　only　based　on　the　distance　vector　between　atoms.　The　graph　convolutional　layer　utilizes　the　radial　basis　function　as　the　attention　mask,　ensuring　the　crystal＇s　rotation　invariance　and　adding　the　geometric　information　on　the　crystal.　Subsequently,　the　average　pooling　layer　is　connected　after　the　convolutional　layer　to　enhance　the　model＇s　ability　to　learn　precise　information.　BNM-CDGNN　connects　multiple　hidden　layers　after　the　average　pooling　layers,　and　these　layers　are　processed　by　the　batch　normalization　layer.　Finally,　the　fully　connected　layer　maps　the　results　to　the　target　property.　BNM-CDGNN　significantly　enhances　the　accuracy　of　crystal　property　prediction　compared　with　previous　baseline　models　such　as　SchNet,　MPNN,　CGCNN,　MEGNet,　and　GATGNN.