This　paper　addresses　the　issues　of　accuracy　and　efficiency　in　extracting　the　distribution　areas　of　water　hyacinth.　A　small-scale　water　hyacinth　dataset　was　established　based　on　drone-collected　photos　of　water　hyacinth　in　river　channels,　which　was　utilized　for　semantic　segmentation　tasks.　A　method　based　on　deep　learning　was　proposed　to　extract　water　hyacinth　distribution　areas　from　high-resolution　drone　remote　sensing　images.　An　efficient,　accurate,　and　automated　convolutional　neural　network　called　AttUNet　was　designed　for　this　purpose,　which　eliminates　the　need　for　manually　designed　rules　and　can　automatically　learn　remote　sensing　features　of　water　hyacinth　and　extract　distribution　areas　from　images,　thereby　improving　the　accuracy　and　efficiency　of　acquiring　relevant　data.　The　research　demonstrates　that　the　proposed　method　can　automatically　extract　features　from　massive　high-resolution　drone　images,　fully　exploring　complex　nonlinear　features,　spectral　features,　and　texture　features　in　high-resolution　drone　images.　The　overall　accuracy　of　extracting　water　hyacinth　distribution　areas　in　the　study　area　reached　98.78%,　with　MIOU　coefficient　and　mRecall　coefficient　of　95.86%　and　98.01%　respectively,　both　of　which　surpass　the　accuracy　indicators　of　Deeplabv3+ and　U-Net.　The　deep　learning　method　for　water　hyacinth　classification　can　fully　exploit　spectral,　texture,　and　latent　feature　information　in　the　data,　and　make　it　more　suitable　for　extracting　water　hyacinth　distribution　information　than　traditional　remote　sensing　classification　methods.　©　The　Author(s)　2025.