The invention relates to a method for segmenting a liver and a focus thereof in a medical image, and the method comprises the steps: firstly carrying out the screening and integration preprocessing ofabdominal CT image data, dividing the abdominal CT image data into a plurality of data sets with different purposes, building a new neural network, and carrying out the initial training through employing small image data; then, storing the trained model, carrying out secondary training by using the original image and a new data enhancement mode, carrying out expansion and corrosion processing onthe predicted image, and evaluating by using a medical evaluation index; through the model prediction results trained by DL, GDL and TL loss functions, adding and averaging the prediction results of the three loss models to form a fusion feature; finally, modifying the network, wherein the three loss models are fused in a single network for training prediction. End-to-end training test can be carried out, liver and focus can be identified at the same time with high precision and high speed, doctors are effectively helped to identify CT images, time and energy consumed by doctors are greatly reduced, and the probability of misdiagnosis is reduced.