Objective To build macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) differentiation model based on the quantitative pre-operative ultrasound features. Methods The hepatocellular carcinoma patients that underwent surgery in our hospital from Augest 1,2017 to Oct 1, 2020 were retrospectively collected. After clinical and pathological sorting, the collected cases were separated into training set (70%) and validation set (30%). The quantitative pre-operative ultrasound features were extracted from training set data and selected by chi-square algorithm. Then, the trained random forest model performance was evaluated on validation set data. Results A total of 79 MTM-HCC and other HCCs were included in this study. The pre-operative AFP level, Edmondson-Steiner grade, satellite lesion and microvascular invasion status distribution were significantly different between MTM-HCC and other HCCs, while the age, sex, HBV infection status was not. The feature selection algorithm used high-dimension texture features in MTM-HCC prediction. The final random forest model achieved AUC=0.895, Accuracy=0.833, Precision=0.833, Sensitivity=60% and Specificity=89.5% on validation set. Conclusions The prognosis-benefit MTM-HCC differentiation model could be built based on the quantitative pre-operative ultrasound features, which has high specificity and was complementary with other differentiation model.