Motivation:　DNA　N6-methyladenine　(6mA)　is　a　pivotal　DNA　modification　for　various　biological　processes.　More　accurate　prediction　of　6mA　methylation　sites　plays　an　irreplaceable　part　in　grasping　the　internal　rationale　of　related　biological　activities.　However,　the　existing　prediction　methods　only　extract　information　from　a　single　dimension,　which　has　some　limitations.　Therefore,　it　is　very　necessary　to　obtain　the　information　of　6mA　sites　from　different　dimensions,　so　as　to　establish　a　reliable　prediction　method.　Results:　In　this　study,　a　neural　network　based　bioinformatics　model　named　GC6mA-Pred　is　proposed　to　predict　N6-methyladenine　modifications　in　DNA　sequences.　GC6mA-Pred　extracts　significant　information　from　both　sequence　level　and　graph　level.　In　the　sequence　level,　GC6mA-Pred　uses　a　three-layer　convolution　neural　network　(CNN)　model　to　represent　the　sequence.　In　the　graph　level,　GC6mA-Pred　employs　graph　neural　network　(GNN)　method　to　integrate　various　information　contained　in　the　chemical　molecular　formula　corresponding　to　DNA　sequence.　In　our　newly　built　dataset,　GC6mA-Pred　shows　better　performance　than　other　existing　models.　The　results　of　comparative　experiments　have　illustrated　that　GC6mA-Pred　is　capable　of　producing　a　marked　effect　in　accurately　identifying　DNA　6mA　modifications.　©　2022　Elsevier　Inc.