In practical applications， multi-view metric learning has become an effective method for handling multi-view data. However， the incompleteness of multi-view data poses significant challenges for multi-view metric learning. Although some methods have attempted to address incomplete multi-view issue， they still have the following shortcomings： 1） most of the existing methods rely on k-Nearest Neighbors （kNN） of the existing samples to fill in missing data， and ignore unique characteristics of samples or views easily； 2） they only utilize the existing sample representations to calculate neighbors， and cannot fully express neighbor relationships between samples. To address these issues， a Dual imputation based Incomplete Multi-View Metric Learning method （DIMVML） was proposed. Firstly， latent features of each view were extracted using a deep autoencoder， and then missing samples were filled in by combining distribution information of samples and difference information between views. Secondly， the results were fused according to quality of the completed samples to obtain higher-quality completion results. Finally， intra-view and inter-view relationships were optimized through a loss function. Experimental results show that in clustering experiments， the proposed method achieves superior accuracy and F1 score on HandWritten， Caltech101-7， Leaves， and YouTubeFace10 datasets compared to advanced multi-view methods such as Subgraph Propagation and Contrastive Calibration （SPCC） and Latent Heterogeneous Graph Network （LHGN）； in classification experiments， the proposed method outperforms other multi-view methods significantly in accuracy on CUB， ORL， and HandWritten datasets.