Dynamic　contrast-enhanced　magnetic　resonance　imaging　provide　not　only　the　information　on　the　morphological　features　of　the　lesions,　but　also　the　changes　of　the　lesion’s　blood　perfusion.　In　this　paper,　we　propose　a　tensor-based　temporal　data　representation　(TTD)　model　and　a　multi-channel　fusion　3D　convolutional　neural　network　(MCF-3D　CNN)　to　extract　the　temporal　and　spatial　features　of　dynamic　contrast　enhanced-MR　images　(DCE-MR　images).　To　evaluate　the　performance　of　the　proposed　methods,　we　established　a　DCE-MR　image　dataset　for　non-invasively　assessing　the　differentiation　of　Hepatocellular　carcinoma　(HCC).　The　TTD　model　achieves　the　accuracy　of　73.96%　for　non-invasive　assessment　of　HCC　differentiation　via　MCF-3D　CNN.　Meanwhile,　the　3D　CNN　with　TTD　achieves　accuracy,　sensitivity　and　specificity　of　95.17%,　96.33%,　and　94.00%,　respectively,　in　discriminating　the　HCC　and　cirrhosis.　Compared　with　the　normal　data　representation　method,　the　proposed　TTD　method　is　more　conducive　for　3D　CNN　to　extract　temporal-spatial　features　of　DCE-MR　images.　©　Springer　Nature　Singapore　Pte　Ltd.,　2018.