Water　quality　prediction　is　of　great　significance　in　pollution　control,　prevention,　and　management.　Deep　learning　models　have　been　applied　to　water　quality　prediction　in　many　recent　studies.　However,　most　existing　deep　learning　models　for　water　quality　prediction　are　used　for　single-site　data,　only　considering　the　time　dependency　of　water　quality　data　and　ignoring　the　spatial　correlation　among　multi-sites.　This　research　defines　and　analyzes　the　non-aligned　spatial　correlations　that　exist　in　multi-site　water　quality　data.　Then　deploy　spatial-temporal　graph　convolution　to　process　water　quality　data,　which　takes　into　account　both　the　temporal　and　spatial　correlation　of　multi-site　water　quality　data.　A　multi-site　water　pollution　prediction　method　called　W-WaveNet　is　proposed　that　integrates　adaptive　graph　convolution　and　Convolutional　Neural　Network,　Long　Short-Term　Memory　(CNN-LSTM).　It　integrates　temporal　and　spatial　models　by　interleaved　stacking.　Theoretical　analysis　shows　that　the　method　can　deal　with　non-aligned　spatial　correlations　in　different　time　spans,　which　is　suitable　for　water　quality　data　processing.　The　model　validates　water　quality　data　generated　on　two　real　river　sections　that　have　multiple　sites.　The　experimental　results　were　compared　with　the　results　of　Support　Vector　Regression,　CNN-LSTM,　and　Spatial-Temporal　Graph　Convolutional　Networks　(STGCN).　It　shows　that　when　W-WaveNet　predicts　water　quality　over　two　river　sections,　the　average　Mean　Absolute　Error　is　0.264,　which　is　45.2%　lower　than　the　commonly　used　CNN-LSTM　model　and　23.8%　lower　than　the　STGCN.　The　comparison　experiments　also　demonstrate　that　W-WaveNet　has　a　more　stable　performance　in　predicting　longer　sequences.