Classifying　river　morphology　is　crucial　for　fluvial　geomorphology　and　hydrology.　River　morphology　reflects　hydrodynamic　and　sedimentary　processes,　providing　critical　insights　into　the　diversity　of　global　river　systems.　This　study　establishes　a　global　framework　for　river　morphology　classification　based　on　remote　sensing　and　topographic　data.　Using　the　Global　Inland　Water　Dynamics　Characterization　dataset　and　the　global　digital　elevation　model　ASTER　GDEM　V3,　a　river　spatial　image　decomposition　process　was　developed,　dividing　global　river　data　into　tens　of　thousands　of　image　blocks　containing　dynamic　imagery　and　elevation　information.　A　ResNet-50　deep　neural　network　was　employed　to　construct　an　image-elevation　fusion　classification　model,　classifying　global　rivers　into　five　major　types:　meandering　rivers,　braided　rivers,　straight　rivers,　anastomosing　rivers,　and　anabranching　rivers.　These　types　were　further　divided　into　17　subtypes　to　capture　finer　morphological　variations.　The　spatial　distribution　patterns　and　morphological　features　of　these　river　types　were　analyzed,　providing　a　comprehensive　understanding　of　the　global　distribution　of　river　planforms.　This　framework　advances　the　knowledge　of　river　systems　at　a　global　scale　and　lays　the　foundation　for　future　studies　in　fluvial　geomorphology　and　hydrology.