Glioma　is　a　primary　brain　tumor　with　high　incidence　rate.　High-grade　gliomas　(HGG)　are　those　with　the　highest　degree　of　malignancy　and　the　lowest　degree　of　survival.　Surgical　resection　and　postoperative　adjuvant　chemoradiotherapy　are　often　used　in　clinical　treatment,　so　accurate　segmentation　of　tumor-related　areas　is　of　great　significance　for　the　treatment　of　patients.　In　order　to　improve　the　segmentation　accuracy　of　HGG,　this　paper　proposes　a　multi-modal　glioma　semantic　segmentation　network　with　multi-scale　feature　extraction　and　multi-attention　fusion　mechanism.　The　main　contributions　are,　(1)　Multi-scale　residual　structures　were　used　to　extract　features　from　multi-modal　gliomas　magnetic　resonance　imaging　(MRI);　(2)　Two　types　of　attention　modules　were　used　for　features　aggregating　in　channel　and　spatial;　(3)　In　order　to　improve　the　segmentation　performance　of　the　whole　network,　the　branch　classifier　was　constructed　using　ensemble　learning　strategy　to　adjust　and　correct　the　classification　results　of　the　backbone　classifier.　The　experimental　results　showed　that　the　Dice　coefficient　values　of　the　proposed　segmentation　method　in　this　article　were　0.909　7,　0.877　3　and　0.839　6　for　whole　tumor,　tumor　core　and　enhanced　tumor　respectively,　and　the　segmentation　results　had　good　boundary　continuity　in　the　three-dimensional　direction.　Therefore,　the　proposed　semantic　segmentation　network　has　good　segmentation　performance　for　high-grade　gliomas　lesions.　Copyright　©2022　Journal　of　Biomedical　Engineering.　All　rights　reserved.