Concerning that the multi-view data analysis is susceptible to the noise of the original dataset and requires additional steps to calculate the clustering results， a Robust Multi-view subspace clustering based on Consistency Graph Learning （RMCGL） algorithm was proposed. Firstly， the potential robust representation of data in the subspace was learned in each view， and the similarity matrix of each view was obtained based on these representations. Then， a unified similarity graph was learned based on the obtained multiple similarity matrices. Finally， by adding rank constraints to the Laplacian matrix corresponding to the similarity graph， the obtained similarity graph had the optimal clustering structure， and the final clustering results were able to be obtained directly by using this similarity graph. The process was completed in a unified optimization framework， in which potential robust representations， similarity matrices and consistency graphs could be learned simultaneously. The clustering Accuracy （ACC） of RMCGL algorithm is 3.36 percentage points， 5.82 percentage points and 5.71 percentage points higher than that of Graph-based Multi-view Clustering （GMC） algorithm on BBC， 100leaves and MSRC datasets， respectively. Experimental results show that the proposed algorithm has a good clustering effect.