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For the issue of unstable network clustering results due to excessive network topology changes in high-speed vehicle movement scenarios, a fast clustering algorithm called SNNCA (Shared Nearest Neighbor Clustering Algorithm) using improved shared-nearest-neighbor-based density peaks clustering is proposed. By comprehensively considering the node's link survival period and movement similarity, a novel node connection stability evaluation metric is proposed. The metric is utilized in the shared nearest neighbor calculation process of the node to organize the network nodes into reasonable multi-hop cluster structure. To adapt to the dynamic changes of the network environment, a cluster maintenance strategy is introduced, where each level of cluster members takes on the task of maintaining the next level of cluster members, and this strategy can perform batch separation or merging of cluster members, achieving distributed and rapid convergence of the algorithm. According to the simulation results of the random deployment scenario, SNNCA algorithm reduces 74% of cluster numbers compared to other newer algorithms, and the average survival time of cluster members increases by nearly 1 time, demonstrating better network stability and robustness.