To　explore　an　effective　non-invasion　medical　imaging　diagnostics　approach　for　hepatocellular　carcinoma　(HCC),　we　propose　a　method　based　on　adopting　the　multiple　technologies　with　the　multi-parametric　data　fusion,　transfer　learning,　and　multi-scale　deep　feature　extraction.　Firstly,　to　make　full　use　of　complementary　and　enhancing　the　contribution　of　different　modalities　viz.　multi-parametric　MRI　images　in　the　lesion　diagnosis,　we　propose　a　data-level　fusion　strategy.　Secondly,　based　on　the　fusion　data　as　the　input,　the　multi-scale　residual　neural　network　with　SPP　(Spatial　Pyramid　Pooling)　is　utilized　for　the　discriminative　feature　representation　learning.　Thirdly,　to　mitigate　the　impact　of　the　lack　of　training　samples,　we　do　the　pre-training　of　the　proposed　multi-scale　residual　neural　network　model　on　the　natural　image　dataset　and　the　fine-tuning　with　the　chosen　multi-parametric　MRI　images　as　complementary　data.　The　comparative　experiment　results　on　the　dataset　from　the　clinical　cases　show　that　our　proposed　approach　by　employing　the　multiple　strategies　achieves　the　highest　accuracy　of　0.847　+/-　0.023　in　the　classification　problem　on　the　HCC　differentiation.　In　the　problem　of　discriminating　the　HCC　lesion　from　the　non-tumor　area,　we　achieve　a　good　performance　with　accuracy,　sensitivity,　specificity　and　AUC　(area　under　the　ROC　curve)　being　0.981　+/-　0.002,　0.981　+/-　0.002,　0.991　+/-　0.007　and　0.999　+/-　0.0008,　respectively.