Multi-view clustering task based on incomplete data has become one of the research hotspots in the field of unsupervised learning. However， most multi-view clustering algorithms based on “shallow” models often find it difficult to extract and characterize potential feature structures within views when dealing with large-scale high-dimensional data. At the same time， the stacking or averaging methods of multi-view information fusion ignore the differences between views and does not fully consider the different contributions of each view to building a common consensus representation. To address the above issues， an Incomplete Multi-View Clustering algorithm based on Self-Attention Fusion （IMVCSAF） was proposed. Firstly， the potential features of each view were extracted on the basis of a deep autoencoder， and the consistency information among views was maximized by using contrastive learning. Secondly， a self-attention mechanism was adopted to recode and fuse the potential representations of each view， and the inherent causality as well as feature complementarity between different views was considered and mined comprehensively. Thirdly， based on the common consensus representation， the potential representation of missing instance was predicted and recovered， thereby fully implementing the process of multi-view clustering. Experimental results on Scene-15， LandUse-21， Caltech101-20 and Noisy-MNIST datasets show that， the accuracy of IMVCSAF is higher than those of other comparison algorithms while meeting the convergence requirements. On Noisy-MNIST dataset with 50% miss rate， the accuracy of IMVCSAF is 6.58 percentage points higher than that of the second best algorithm — COMPETER （inCOMPlete muLti-view clustEring via conTrastivE pRediction）.