In real applications, it is an important field for clustering the multi-view data in data mining. The incompleteness of multi- view caused by missing samples brings great challenge to multi-view clustering task. The shallow graph structure information is easily affected by noise and missing data. Most of the existing multi-view clustering methods are difficult to describe the underlying structure of all views accurately and comprehensively, which reduces the performance of incomplete multi-view clustering. To this end, this study proposes a robust incomplete multi-view clustering algorithm based on the strategies of diffusing and fusing among multi-order neighborhoods. Firstly, the proposed algorithm obtains the potential structural information from incomplete views by utilizing multi-order similarities. Then, the deep structural information of multi-views is nonlinearly fused by the way of cross-view diffusion. Through all above, the much more comprehensive structural information among views can be extracted from the proposed algorithm, thereby reducing the uncertainty of views-structure caused by missing samples. In addition, this paper presents detailed steps to prove the convergence of the proposed algorithm. Experimental results show that the proposed method is more effective in solving the problem of incomplete multi-view clustering than other existing methods.