Multi-view clustering faces serious challenges in reducing computational and memory demands for large-scale datasets while effectively extracting structural information from multi-view data. Most existing methods address algorithmic complexity by introducing anchors, typically through a two-stage process involving anchor sampling and subsequent bipartite graph construction. However, the quality of anchor selection directly affects the performance of the bipartite graph, this two-stage mechanism lacks mutual optimization, thereby negatively impacting clustering performance. To address these issues, we propose the Adaptive Unified Framework with Global Anchor Graph for Large-scale Multi-view Clustering (AUF-LMC). Different from the traditional sample-based anchor selection mechanism, AUF-LMC adaptively learns the underlying anchors across multiple views and builds global bipartite graph on this basis, so that these two processes can be linked to each other to promote optimization and improve clustering performance. Furthermore, we unify all processes within a single framework and apply appropriate constraints to the bipartite graph. Experimental evaluations demonstrate that our method delivers superior clustering performance and efficiency, characterized by fast convergence and robustness on standard datasets.