Abstract: The road identification of off-road vehicles can be carried out according to the dynamic response on different road surfaces, which lays a foundation for adjusting the parameters of the chassis control subsystem to obtain better driving performance. However, it is difficult to analyze the response mechanism of vehicles on different road surfaces due to the complexity of the off-road environment, which brings challenges to accurate road recognition based on vehicle dynamic response. In this paper, an SHAP-RF road recognition algorithm design framework is proposed, which realizes dimensionality reduction of the high-dimensional RF (random forest) road recognition model through the SHAP (Shapley additive explanations) model interpretation method. Firstly, we collected the driving data of the test vehicle on soil road, sand road, good asphalt road, and snow-icing road, and then three secondary driving features were calculated. Furthermore, a total of 105 features of driving data were calculated, including time domain features and frequency domain features. A high-dimensional RF road recognition model was established with all the features as input. The SHAP interpretation method was used to analyze the influence of input features on the recognition results in the high-dimensional model, and the correlation between each feature and road type was extracted to complete feature screening. Finally, a dimensional-reduction RF road recognition model is designed using the selected features. The validation test of the algorithm based on real vehicle data shows that the identification accuracy rate of the dimensional-reduction road recognition model is above 94% for all four kinds of road, and the recall rate is above 93%. Compared with the high-dimensional RF road recognition model, the accuracy rate and recall rate on all kinds of roads drop by no more than 3.2%. This proves that the proposed SHAP-RF road recognition algorithm design framework can reduce the number of input features while ensuring the recognition accuracy of road categories.