In order to solve the problems of uncertainty in completing missing view data， lack of robustness of embedding learning and low model generalization in traditional deep incomplete multi-view clustering algorithms， an Incomplete Multi-View Clustering algorithm based on Attention Mechanism （IMVCAM） was proposed. Firstly， K-Nearest Neighbors （KNN） algorithm was used to complete the missing data in the view， making the training data complementary. Then， after passing the linear encoding layer， the obtained embedding was passed through the attention layer to improve the quality of the embedding. Finally， the embedding obtained from the training of each view was clustered using k-means clustering algorithm， and the weights of the views were determined by the Pearson correlation coefficient. Experimental results on five classic datasets show that， the optimal result was achieved by IMVCAM on Fashion dataset， compared with the sub-optimal Deep Safe Incomplete Multi-View Clustering （DSIMVC） algorithm， IMVCAM improves the clustering accuracy by 2.85 and 4.35 percentage points respectively when the data missing rate is 0.1 and 0.3. Besides， on Caltech101-20 dataset， the clustering accuracy of IMVCAM is increased by 7.68 and 3.48 percentage points respectively compared to that of the sub-optimal algorithm IMVCSAF （Incomplete Multi-View Clustering algorithm based on Self-Attention Fusion） when the missing rate is 0.1 and 0.3. The proposed algorithm can effectively deal with the incompleteness of multi-view data and the problem of model generalization.