Purpose.　To　evaluate　the　diagnostic　performance　of　deep　learning　with　a　multichannel　fusion　three-dimensional　convolutional　neural　network　(MCF-3DCNN)　in　the　differentiation　of　the　pathologic　grades　of　hepatocellular　carcinoma　(HCC)　based　on　dynamic　contrast-enhanced　magnetic　resonance　images　(DCE-MR　images).　Methods　and　Materials.　Fifty-one　histologically　proven　HCCs　from　42　consecutive　patients　from　January　2015　to　September　2017　were　included　in　this　retrospective　study.　Pathologic　examinations　revealed　nine　well-differentiated　(WD),　35　moderately　differentiated　(MD),　and　seven　poorly　differentiated　(PD)　HCCs.　DCE-MR　images　with　five　phases　were　collected　using　a　3.0　Tesla　MR　scanner.　The　4D-tensor　representation　was　employed　to　organize　the　collected　data　in　one　temporal　and　three　spatial　dimensions　by　referring　to　the　phases　and　3D　scanning　slices　of　the　DCE-MR　images.　A　deep　learning　diagnosis　model　with　MCF-3DCNN　was　proposed,　and　the　structure　of　MCF-3DCNN　was　determined　to　approximate　clinical　diagnosis　experience　by　taking　into　account　the　significance　of　the　spatial　and　temporal　information　from　DCE-MR　images.　Then,　MCF-3DCNN　was　trained　based　on　well-labeled　samples　of　HCC　lesions　from　real　patient　cases　by　experienced　radiologists.　The　accuracy　when　differentiating　the　pathologic　grades　of　HCC　was　calculated,　and　the　performance　of　MCF-3DCNN　in　lesion　diagnosis　was　assessed.　Additionally,　the　areas　under　the　receiver　operating　characteristic　curves　(AUC)　for　distinguishing　WD,　MD,　and　PD　HCCs　were　calculated.　Results.　MCF-3DCNN　achieved　an　average　accuracy　of　0.7396　+/-　0.0104　with　regard　to　totally　differentiating　the　pathologic　grade　of　HCC.　MCF-3DCNN　also　achieved　the　highest　diagnostic　performance　for　discriminating　WD　HCCs　from　others,　with　an　average　AUC,　accuracy,　sensitivity,　and　specificity　of　0.96,　91.00%,　96.88%,　and　89.62%,　respectively.　Conclusions.　This　study　indicates　that　MCF-3DCNN　can　be　a　promising　technology　for　evaluating　the　pathologic　grade　of　HCC　based　on　DCE-MR　images.