Hepatocellular　carcinoma　(HCC)　is　a　type　of　primary　malignant　tumor　and　an　urgent　problem　to　be　solved,　particularly　in　China,　one　of　the　countries　with　the　highest　prevalence　of　HCC.　In　the　choice　of　treatment　methods　for　patients　with　hepatocellular　carcinoma,　accurate　histological　grading　of　the　lesion　area　undoubtedly　plays　a　vital　role　that　helps　the　management　and　therapy　of　HCC　patients.　However,　the　current　pathological　detection　as　the　gold　standard　has　defects,　such　as　invasiveness　and　a　large　sampling　error.　Therefore,　it　is　an　important　direction　of　intelligent　medical　treatment　to　provide　noninvasive　and　accurate　lesion　grading　using　imaging　technology　combined　with　artificial　intelligence　technology.　On　the　basis　of　the　radiologists＇　experience　in　reading　clinical　images,　this　paper　proposed　a　self-attentional　guidance-based　histological　differentiation　discrimination　model　combined　with　multi-modality　fusion　and　an　attention　weight　calculation　scheme　for　dynamic　contrast-enhanced　magnetic　resonance　imaging　(DCE-MRI)　sequences　of　hepatocellular　carcinoma.　The　model　combined　the　spatiotemporal　information　contained　in　the　enhancement　sequence　and　learned　the　importance　of　each　sequence　and　the　slice　in　the　sequence　for　the　classification　task.　It　effectively　used　the　feature　information　contained　in　the　enhancement　sequence　in　the　temporal　and　spatial　dimensions　to　improve　the　classification　performance.　During　the　experiment,　the　model　was　trained　and　tested　on　the　clinical　data　set　of　the　top　three　hospitals　in　China.　The　experimental　results　show　that　the　self-attention-guided　model　proposed　in　this　paper　achieves　higher　classification　performance　than　several　benchmark　and　mainstream　models.　Comprehensive　experiments　were　performed　on　the　clinical　dataset　with　labels　annotated　by　professional　radiologists.　The　results　show　that　our　proposed　self-attention　model　can　achieve　acceptable　quantitative　measuring　of　HCC　histologic　grading　based　on　the　MRI　sequences.　In　the　WHO　histological　classification　task,　the　classification　accuracy　of　the　proposed　model　reaches　80%,　the　sensitivity　is　82%,　and　the　precision　is　82%.　©　2021,　Science　Press.　All　right　reserved.