Hepatocellular　carcinoma　(HCC)　is　a　type　of　primary　liver　malignant　tumor　with　a　high　recurrence　rate　and　poor　prognosis　even　undergoing　resection　or　transplantation.　Accurate　discrimination　of　the　histologic　grades　of　HCC　plays　a　critical　role　in　the　management　and　therapy　of　HCC　patients.　In　this　paper,　we　discuss　a　deep　learning-based　diagnostic　model　for　HCC　histologic　grading　with　multimodal　Magnetic　Resonance　Imaging　(MRI)　images　to　overcome　the　problem　of　limited　well-annotated　data　and　extract　the　discriminated　fusion　feature　referring　to　the　clinical　diagnosis　experience　of　radiologists.　Accordingly,　we　propose　a　novel　Multimodality-Contribution-Aware　TripNet　(MCAT)　based　on　the　metric　learning　and　the　attention-aware　weighted　multimodal　fusion.　The　novelty　of　the　method　lies　in　the　multimodality　small-shot　learning　architecture　designation　and　the　multimodality　adaptive　weighted　computing　scheme.　The　comprehensive　experiments　are　done　on　the　clinic　dataset　with　the　well-annotation　of　lesion　location　by　the　professional　radiologist.　The　experimental　results　show　that　our　proposed　MCAT　is　not　only　able　to　achieve　acceptable　quantitative　measuring　of　HCC　histologic　grading　based　on　the　MRI　sequences　with　small　cases　but　also　outperforms　previous　models　in　HCC　histologic　grading,　reaching　an　accuracy　of　84　percent,　a　sensitivity　of　87　percent　and　precision　of　89　percent.