Low-Rank Representation（LRR） has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. However, conventional LRR-based methods simply use Euclidean distance to measure the similarity of samples, where cannot reflect the inherent geometric structure of data with manifold structure. Meanwhile, recent studies have shown that a probabilistically motivated distance measurement（called effective distance） can effectively model the global information of data to measure the similarity between samples. To this end, this paper proposes an Effective Distance Based Low-Rank Representation（EDLRR） model, which firstly uses the sparse representation method to calculate the effective distance between samples for constructing a Laplacian matrix, and then develops a Laplacian regularized low-rank representation term. Low rank representation model. This method can not only represent the global low-dimensional structure, but also capture the geometric structure information in the data of the manifold structure. To evaluate the effectiveness of the proposed method, this paper conducts classification experiments by using three public datasets. Experimental results show that the proposed EDLRR method has higher classification performance and stronger robustness than the traditional Euclidean distance based methods.