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Since the traditional semi-supervised multi-view algorithms seldom take into account the diversity of information contained in different views and neglect the consistency of spatial structure between different views, they hardly achieve promising performance when dealing with multi-view data with noise and outlying entries. Although some researchers have proposed semi-supervised multi-view methods,these methods do not make full use of sample discriminant information and subspace structure information under different metric learning,which leads to the unsatisfactory classification results. To deal with the above problems,this paper proposes a semi-supervised multi-view classification via consistency constraint (SMCC) for multi-view data analysis. Firstly, the consistency constraints between different views are enhanced based on the Hilbert-Schmidt independence criteria (HSIC). Then, the dimensionality reduction is performed by feature projection to preserve the local manifold structure, which is integrated with Frobenius norm constraint to improve the robustness of the algorithm. Furthermore, the corresponding weights are adaptively assigned to different views to reduce the influence of feature information and noise pollution in different views. Finally, the proposed model can be solved efficiently using the linear alternative direction method with adaptive penalty and eigen-decomposition. The experimental results on four benchmark datasets show that the proposed algorithm can discover more effective discriminant information from multi-view data and its accuracy is improved.