The　development　of　BACE-1　(beta-site　amyloid　precursor　protein　cleaving　enzyme　1)　inhibitors　is　a　crucial　focus　in　exploring　early　treatments　for　Alzheimer＇s　disease　(AD).　Recently,　graph　neural　networks　(GNNs)　have　demonstrated　significant　advantages　in　predicting　molecular　activity.　However,　their　reliance　on　graph　structures　alone　often　neglects　explicit　sequence-level　semantic　information.　To　address　this　limitation,　we　proposed　a　Graph　and　multi-level　Sequence　Fusion　Learning　(GSFL)　model　for　predicting　the　molecular　activity　of　BACE-1　inhibitors.　Firstly,　molecular　graph　structures　generated　from　SMILES　strings　were　encoded　using　GNNs　with　an　atomic-level　characteristic　attention　mechanism.　Next,　substrings　at　functional　group,　ion　level,　and　atomic　level　substrings　were　extracted　from　SMILES　strings　and　encoded　using　a　BiLSTM-Transformer　framework　equipped　with　a　hierarchical　attention　mechanism.　Finally,　these　features　were　fused　to　predict　the　activity　of　BACE-1　inhibitors.　A　dataset　of　1548　compounds　with　BACE-1　activity　measurements　was　curated　from　the　ChEMBL　database.　In　the　classification　experiment,　the　model　achieved　an　accuracy　of　0.941　on　the　training　set　and　0.877　on　the　test　set.　For　the　test　set,　it　delivered　a　sensitivity　of　0.852,　a　specificity　of　0.894,　a　MCC　of　0.744,　an　F1-score　of　0.872,　a　PRC　of　0.869,　and　an　AUC　of　0.915.　Compared　to　traditional　computer-aided　drug　design　methods　and　other　machine　learning　algorithms,　the　proposed　model　can　effectively　improve　the　accuracy　of　the　molecular　activity　prediction　of　BACE-1　inhibitors　and　has　a　potential　application　value.