［Purposes］ The paper aims to predict the slope prevention cost for mountainous operating expressway.［Methods］ Taken the mountainous operating expressway in Anhui Province as the research object, based on the comprehensive consideration of 14 characteristic indexes in 3 aspects of basic situation, prevention scheme and price factor for the slope, a prediction model of the slope prevention cost for mountainous operating expressway was established by using the particle swarm optimization (PSO) algorithm to optimize the support vector machine (SVM). The penalty factor and kernel function parameters of SVM were optimized by the PSO algorithm. According to the engineering examples, the prediction performance of the established model was verified and evaluated by relative error, root mean square error and determination coefficient etc., and compared with that of the other models.［Findings］ The average relative error decreases by 41.9%, and the determination coefficient reaches 0953 when using the established model to predict the slope prevention cost for mountainous operating expressway.［Conclusions］ The model proposed in this paper has higher accuracy and applicability, and can provide some reference for decision-making of slope prevention.