In order to reasonably and efficiently carry out the risk assessment of road tunnel construction collapse, the risk assessment model of road tunnel construction collapse was studied by Rough Set (RS), Grid Search Method and Support Vector Classification (SVC). First, integrate advanced geological prediction to build an index system for risk assessment of highway tunnel construction collapse. At the same time, collect 100 tunnel collapse related case information and discretize the index data. Secondly, attribute reduction is conducted based on the condition information entropy of rough set to obtain the reduced core index set. Then, grid search method is used to find the optimal parameters of the support vector classification training set, The risk assessment model of highway tunnel construction collapse based on Rough Set-Grid Search-Support Vector Classification (RS-GS-SVC) is established. Finally, the model is used to predict the test samples. The results show that under the condition of the same learning sample, compared with Rough Set-Genetic Algorithm-Support Vector Classification (RS-GA-SVC) model and Rough Set-Particle Swarm Optimization-Support Vector Classification (RS-PSO-SVC) model, RS-GS-SVC model has higher classification accuracy; Under the same proportion of training set and test set, the prediction accuracy of RS-GS-SVC model is higher than that of GS-SVC model, with the accuracy rates of 93.33% and 90% respectively, and the operation time of RS-GS-SVC model is shorter. It can clearly be seen that the model complexity is effectively reduced and the classification accuracy is improved through the reduction of rough set conditional information entropy attributes.