Most of the existing multi-view clustering algorithms assume that there is a linear relationship between multi-view data points， and fail to maintain the locality of original feature space during the learning process. At the same time， merging subspace in Euclidean space is too rigid to align learned subspace representations. To solve the above problems， a multi-view clustering algorithm via subspaces merging on Grassmann manifold was proposed. Firstly， the kernel trick and the learning of local manifold structure were combined to obtain the subspace representations of different views. Then， the subspace representations were merged on the Grassmann manifold to obtain the consensus affinity matrix. Finally， spectral clustering was performed on the consensus affinity matrix to obtain the final clustering result. And Alternating Direction Method of Multipliers （ADMM） was used to optimize the proposed model. Compared with Kernel Multi-view Low-Rank Sparse Subspace Clustering （KMLRSSC） algorithm， the proposed algorithm has the clustering accuracy improved by 20.83 percentage points， 9.47 percentage points and 7.33 percentage points on MSRCV1， Prokaryotic and Not-Hill datasets. Experimental results verify the effectiveness and good performance of the multi-view clustering algorithm via subspace merging on Grassmann manifold.