A liver boundary identification method implemented by a liver boundary identification system, comprising: obtaining liver tissue information of a liver tissue to be identified; wherein the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue, partitioning the liver tissue information into a plurality of detection sub-regions; calculating a feature value of the liver tissue information in each of the detection sub-regions, and determining the liver tissue boundary according to the feature value of the liver tissue information in the detection sub-regions; and outputting position information of the identified liver tissue boundary characterized in that , the calculating a feature value of the liver tissue information in each of the detection sub-regions, and determining the liver tissue boundary according to the feature value of the liver tissue information in the detection sub-regions comprises: calculating a Nakagami distribution value m i for a one-dimensional ultrasonic signal R i of the liver tissue in each detection sub-region S i ; and calculating a weight W i of each detection sub-region S i according to the following formula, and determining the detection sub-region corresponding to a maximum weight value to be a boundary region of the liver tissue: W i = 100 ∗ m i d i where d i is scanning depth corresponding to the detection sub-region S i , and i is a natural number. obtaining liver tissue information of a liver tissue to be identified; wherein the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue, partitioning the liver tissue information into a plurality of detection sub-regions; calculating a feature value of the liver tissue information in each of the detection sub-regions, and determining the liver tissue boundary according to the feature value of the liver tissue information in the detection sub-regions; and outputting position information of the identified liver tissue boundary characterized in that , the calculating a feature value of the liver tissue information in each of the detection sub-regions, and determining the liver tissue boundary according to the feature value of the liver tissue information in the detection sub-regions comprises: calculating a Nakagami distribution value m i for a one-dimensional ultrasonic signal R i of the liver tissue in each detection sub-region S i ; and calculating a weight W i of each detection sub-region S i according to the following formula, and determining the detection sub-region corresponding to a maximum weight value to be a boundary region of the liver tissue: W i = 100 ∗ m i d i where d i is scanning depth corresponding to the detection sub-region S i , and i is a natural number. calculating a Nakagami distribution value m i for a one-dimensional ultrasonic signal R i of the liver tissue in each detection sub-region S i ; and calculating a weight W i of each detection sub-region S i according to the following formula, and determining the detection sub-region corresponding to a maximum weight value to be a boundary region of the liver tissue: W i = 100 ∗ m i d i where d i is scanning depth corresponding to the detection sub-region S i , and i is a natural number. <EOS>