Probabilistic Graph Representation Learning （GRL） models typically rely on KL （Kullback-Leibler） divergence to constrain latent distribution of node representations， and suffer from posterior collapse， support set sensitivity， and unstable training gradient. Therefore， we proposed a GRL model based on Wasserstein distance — Wasserstein Graph AutoEncoder （WGAE） to use the closed-form solution of second-order Wasserstein distance as a regularization term to constrain latent distribution from the perspective of optimal transport theory. Compared to KL divergence， the advantage of Wasserstein distance lies in the robustness of support set and gradient continuity， which enables probabilistic graph representation learning models to avoid representation degradation caused by posterior collapse， encourages models to learn potential representations with clearer geometric structure and more disentangled dimensions， and optimize training dynamics. Experimental results demonstrate that WGAE not only improves the prediction performance of graph representation learning models in downstream tasks， but also enhances representation quality and training dynamics. At the same time， through multiple experiments on benchmark graph datasets， the proposed model’s advantages in prediction accuracy， training stability， information encoding efficiency， resistance to data perturbations， and embedding space entanglement resolution are verified comprehensively. It can be seen that Wasserstein regularization can complement and enhance various advanced GRL mechanisms， bringing stable performance gains， and providing a practical basis for building more robust and efficient GRL optimization components.