The　early　detection　of　hepatic　fibrosis　is　of　critical　importance.　Ultrasound　backscattered　radiofrequency　signals　from　the　liver　contain　abundant　information　about　its　microstructure.　We　proposed　a　method　for　characterizing　human　hepatic　fibrosis　using　one-dimensional　convolutional　neural　networks　(CNNs)　based　on　ultrasound　backscattered　signals.　The　proposed　CNN　model　was　composed　of　four　one-dimensional　convolutional　layers,　four　one-dimensional　max-pooling　layers,　and　four　fully　connected　layers.　Ultrasound　radiofrequency　signals　collected　from　230　participants　(F0:　23;　F1:　46;　F2:　51;　F3:　49;　F4:　61)　with　a　3-MHz　transducer　were　analyzed.　Liver　regions　of　interest　(ROIs)　that　contained　most　of　the　liver　ultrasound　backscattered　signals　were　manually　delineated　using　B-mode　images　reconstructed　from　the　backscattered　signals.　ROI　signals　were　normalized　and　augmented　by　using　a　sliding　window　technique.　After　data　augmentation,　the　radiofrequency　signal　segments　were　divided　into　training　sets,　validation　sets　and　test　sets　at　a　ratio　of　80%:10%:10%.　In　the　test　sets,　the　proposed　algorithm　produced　an　area　under　the　receive　operating　characteristic　curve　of　0.933　(accuracy:　91.30%;　sensitivity:　92.00%;　specificity:　90.48%),　0.997　(accuracy:　94.29%;　sensitivity:　94.74%;　specificity:　93.75%),　0.818　(accuracy:　75.00%;　sensitivity:　69.23%;　specificity:　81.82%),　and　0.934　(accuracy:　91.67%;　sensitivity:　88.89%;　specificity:　94.44%)　for　diagnosis　liver　fibrosis　stage　＞=　F1,　＞=　F2,　＞=　F3,　and　＞=　F4,　respectively.　Experimental　results　indicated　that　the　proposed　deep　learning　algorithm　based　on　ultrasound　backscattered　signals　yields　a　satisfying　performance　when　diagnosing　hepatic　fibrosis　stages.　The　proposed　method　may　be　used　as　a　new　quantitative　ultrasound　approach　to　characterizing　hepatic　fibrosis.