Manifold learning is one of the most important research directions nowadays. The performance of manifold learning methods is affected by the choice of reduced dimension. When the reduced dimension is the intrinsic dimension, it is easily to handle the original data. However, intrinsic dimension estimation is still a challenge of manifold learning. In this study, a novel unsupervised method is proposed, called similar manifold learning based on selective cluster ensemble (SML-SCE), which avoids the estimation of intrinsic dimension and achieves a promising performance. SML-SCE generates representative anchors with modified balanced K-means based hierarchical K-means (MBKHK) to construct similarity matrix efficiently. Moreover, multiple similar low-dimensional embeddings in different dimensions are obtained, which are the different presentations of original data. The diversity of these similar low-dimensional embeddings is benefit to the ensemble learning. Therefore, selective cluster ensemble method is taken advantage of as the combination rule. For the clustering results obtained by K-means in similar low-dimensional embeddings, the normalized mutual information (NMI) is calculated between clusterings as weight. Finally, the low weight clusterings is discarded and a selective vote scheme is adopted based on weight to obtain the final clustering. Extensive experiments on several data sets demonstrate the validity of the proposed method.