The existing global manifold learning algorithms are relatively sensitive to the neighborhood size, which is difficult to select efficiently. The reason is mainly because the neighborhood graph is constructed based on Euclidean distance, by which shortcut edges tend to be introduced into the neighborhood graph. To overcome this problem, a global manifold learning algorithm is proposed based on random walk, called the random walk-based isometric mapping (RW-ISOMAP). Compared with Euclidean distance, the commute time distance, achieved by the random walk on the neighborhood graph, can measure the similarity between the given data within the nonlinear geometric structure to a certain extent, thus it can provide robust results and is more suitable to construct the neighborhood graph. Consequently, by constructing the neighborhood graph based on the commute time distance, RW-ISOMAP is less sensitive to the neighborhood size and more robust than the existing global manifold learning algorithms. Finally, the experiment verifies the effectiveness of RW-ISOMAP.