To address the inconsistency between attribute completion and clustering objectives in two-stage methods and the low efficiency of deep learning methods for attribute-missing graph clustering， an Attribute-Missing Graph Clustering model based on Stacked Joint Optimization （AMGC-SJO） was proposed. First， a Matrix Factorization-based Attribute-Missing Graph Clustering model （AMGC-MF） was constructed， which introduced an enhanced adjacency matrix to represent global node connection relations and employed graph regularization for attribute completion， and then a joint Non-negative Matrix Factorization （NMF） was applied to the completed the attribute matrix and enhanced adjacency matrix， so as to learn the node clustering degrees of membership. On this basis， an AMGC-SJO was further designed to update the attribute matrix and enhanced adjacency matrix dynamically during iteration， thereby enabling co-optimization of attribute completion and clustering tasks. Experimental results show that AMGC-SJO outperforms AMGC-MF on multiple clustering metrics， with better key metrics on five datasets. Compared to Attribute-Missing Graph Clustering （AMGC）， the proposed model achieves comparable clustering accuracy while reducing running time by at least 89.57% under the topological centrality-based missing mechanism. Furthermore， on the Cora dataset and attribute privacy missing mechanism， when the missing rate is increased from 10% to 90%， the clustering metrics of AMGC-SJO fluctuate within 2.08 percentage points， demonstrating strong robustness， whereas those of AMGC decline by over 30.88 percentage points. It can be seen that the proposed model provides an accurate， efficient， and robust solution for attribute-missing graph clustering.