Over　the　past　decades　of　rapid　development,　the　global　water　pollution　problem　became　prominent.　Accurate　water　quality　prediction　can　detect　the　trend　and　anomaly　of　water　quality　changes　in　advance,　thereby　taking　timely　measures　to　avoid　water　quality　problems.　Traditional　statistical　methods　for　water　quality　prediction　tend　to　fail　to　capture　the　complex　relationship　among　multiple　water　quality　variables.　Deep　learning　models　face　a　challenge　to　capture　both　temporal　dependence　and　spatial　correlation　of　the　water　quality　series　data.　To　solve　the　above　problems,　this　work　proposes　an　adaptive　and　dynamic　graph　fusion　water　quality　prediction　model　based　on　a　spatiotemporal　attention　mechanism　named　Spatial-Temporal　Graph　Fusion　Transformer　(STGFT).　It　integrates　a　spatial　attention　encoder,　a　temporal　attention　encoder,　an　adaptive　dynamic　adjacency　matrix　generator,　and　a　multi-graph　fusion　layer.　Among　them,　the　first　two　are　adopted　to　capture　the　spatial　correlations　and　temporal　characteristics　among　different　water　quality　monitoring　stations,　respectively.　The　generator　can　produce　adaptive　and　dynamic　adjacency　matrices　to　reflect　potential　spatial　relationships　in　a　river　network.　Experimental　results　with　real-life　water　quality　datasets　reveal　that　the　prediction　accuracy　of　STGFT　outperforms　the　existing　state-of-the-art　models.　©　2004-2012　IEEE.