Accurately　predicting　dissolved　oxygen　(DO)　is　essential　for　water　environment　protection　and　management.　The　spatiotemporal　dependencies　of　water　quality　and　the　interactions　between　indicators　are　neglected　in　existing　prediction　models.　To　improve　the　DO　prediction　accuracy,　a　graph　neural　network　based　on　indicator　attention　mechanism　and　bayesian　optimization　(BO-AM-MTGNN)　was　proposed　in　this　study.　Hourly　water　quality　data　at　20　sampling　sites　in　the　Chaohu　Lake　basin　from　January　2022　to　February　2024　were　used　as　the　research　dataset.　The　effectiveness　of　the　BO-AM-MTGNN　model　was　validated　through　comparisons　with　baseline　models　(XGBoost,　LightGBM,　LSTM,　GRU,　Informer)　and　ablation　experiment　(BO-AM-MTGNN,　AM-MTGNN,　MTGNN).　The　results　demonstrated　that　the　BO-AM-MTGNN　model　effectively　captured　the　temporal　and　spatial　information　of　water　quality　data.　Correlations　between　indicators　can　be　fully　extracted　by　the　indicator　attention　mechanism.　Compared　with　the　MTGNN　model,　the　MAE,　RMSE,　and　MAPE　of　the　BO-AM-MTGNN　model　decreased　by　12.16　%,　5.50　%,　and　12.13　%,　respectively.　The　prediction　accuracy　of　MTGNN　outperformed　the　baseline　models,　with　the　performance　ranking　as　follows:　MTGNN　＞　Informer　＞　LSTM　＞　GRU　＞　LightGBM　＞　XGBoost.　The　BO-AM-MTGNN　model　proposed　in　this　study　effectively　improves　DO　prediction　accuracy.　In　future　studies,　the　BO-AM-MTGNN　model　holds　potential　for　water　quality　early　warning　and　pollution　source　tracking.