With the increase of the volume of data， multi-view clustering with missing view data is becoming progressively common， which is regarded as the incomplete multi-view clustering. Powered by the development of deep learning models， clustering models introduced deep learning can normally get more outstanding performance than shallow models. A novel deep incomplete multi-view clustering model is proposed， which is called improved self-paced deep incomplete multi-view clustering. In this model， the complementarity of multi-view data is fully considered， and the missing views are completed by the nearest neighbor imputation scheme based on multi-view data characteristics. Multiple encoders are exerted to obtain the low-dimensional potential features of multiple views. Meanwhile， the graph embedding strategy is introduced to maintain the geometric structure among the potential features. The consistency principle is exerted to fuse the potential features from different views to obtain consistent potential features. Experimental results indicate that， compared with the existing incomplete multi-view clustering models， our model can deal with various incomplete multi-view clustering more flexibly and efficiently， thus improving the robustness and performance of incomplete multi-view clustering.