The existing multiview graph learning methods are mainly based on the premise that the data has good completeness, and do not fully consider the learning problem of incomplete data due to lack of features. Facing this problem, this paper proposes a multiview graph learning method of incomplete data. On one hand, data reconstruction and graph learning are put into the same framework from a local perspective. The view-specific neighbor relations can be learned from reconstruction data by incomplete data compensation, remedying the influence on data distribution due to feature missing. On the other hand, in order to maintain the two-dimensional structure of the nearest neighbor graph, tensor analysis is introduced to construct the fusion graph learning constraints based on multiple perspectives from a global perspective, and the capture the high-order potential relevance of graph structure between perspectives under missing data. This framework alternately optimizes data reconstruction, perspective specific graph learning and fusion tensor graph structure learning, so as to promote each other in iteration and effectively improve the learning ability of the model for incomplete multi perspective data. The proposed graph learning method is applied to two kinds of incomplete data spectral clustering experiments. The experimental results demonstrate that the proposed method outperforms the existing mainstream multiview clustering methods on both of evaluations and robustness.