The　evaluation　of　pediatric　hepatic　steatosis　and　early　detection　of　fatty　liver　in　children　are　of　critical　importance.　In　this　paper,　a　deep　learning　model　based　on　the　convolutional　neural　network　(CNN)　of　ultrasound　backscattered　signals,　multi-branch　residual　network　(MBR-Net),　was　proposed　for　characterizing　pediatric　hepatic　steatosis.　The　MBR-Net　was　composed　of　three　convolutional　branches.　Each　branch　used　different　sizes　of　convolution　blocks　to　enhance　the　capability　of　local　feature　acquisition,　and　leveraged　the　residual　mechanism　with　skip　connections　to　guide　the　network　to　effectively　capture　features.　A　total　of　393　frames　of　ultrasound　backscattered　signals　collected　from　131　children　were　included　in　the　experiments.　The　hepatic　steatosis　index　was　used　as　the　reference　standard　for　diagnosing　the　steatosis　grade,　G0-G3.　The　ultrasound　backscattered　signals　within　the　liver　region　of　interests　(ROIs)　were　normalized　and　augmented　using　a　sliding　gate　method.　The　gated　ROI　signals　were　randomly　divided　into　training,　validation,　and　test　sets　with　the　ratio　of　8:1:1.　The　area　under　the　operating　characteristic　curve　(AUC),　accuracy　(ACC),　sensitivity　(SEN),　and　specificity　(SPE)　were　used　as　the　evaluation　metrics.　Experimental　results　showed　that　the　MBR-Net　yields　AUCs　for　diagnosing　pediatric　hepatic　steatosis　grade　&　GE;G1,　&　GE;G2,　and　&　GE;G3　of　0.94　(ACC:　93.65%;　SEN:　89.79%;　SPE:　84.48%),　0.93　(ACC:　90.48%;　SEN:　87.75%;　SPE:　82.65%),　and　0.93　(ACC:　87.76%;　SEN:　84.84%;　SPE:　86.55%),　respectively,　which　were　superior　to　the　conventional　one-branch　CNNs　without　residual　mechanisms.　The　proposed　MBR-Net　can　be　used　as　a　new　deep　learning　method　for　ultrasound　backscattered　signal　analysis　to　characterize　pediatric　hepatic　steatosis.