[Objective] A random forest evaluation model for physical toughness of slopes along mountain roads was established in order to provide a scientific reference for disaster prevention in mountain areas. [Methods] Taking Maoxian County, Sichuan Province as the research area, this study selected 13 physical toughness assessment factors of slopes, including elevation, aspect, slope direction, slope position, micro landform, curvature, type of slope, normalized vegetation index, lithology, distance from water system, distance from fault, distance from road and annual average rainfall, and combined with 498 historical slope failure points along the highway, to construct a geospatial information database for the evaluation of slope physical toughness. The sample data was divided into training data and validation data according to the proportion of 7∶3. The random forest method was used to train and model the training data, then the obtained model was used to predict and analyze the training data, the validation data and the overall sample data respectively. And confusion matrix and ROC curve were used to verify the accuracy of the model prediction. [Results] Among the evaluation factors, the weight of elevation, distance from the road, and distance from the water system was higher. The accuracy of the model was high, the accuracy of confusion matrix was 98.9%, and the AUC (area under the ROC curve) values of the training data, the validation data and the overall sample data was 1.000，0.870 and 0.978, respectively. The model was simulated into the entire study area, and the physical toughness of the study area was divided into 5 levels as: extremely low, low, medium, high and extremely high. [Conclusions] The physical toughness evaluation model of the slope along the mountain highway based on the random forest method has high stability and reliability.