<p>Multi-view clustering has important applications in machine learning and computer vision, but existing methods face three major limitations: insufficient consideration of the differences among views, failure to capture the complex relationships among views, and neglect of the interdependence of embeddings and clustering labels. To overcome these limitations, this paper proposes a novel dependency-driven spectral embedding based multi-view clustering (DDSE), which introduces a dual-layer learning system. On the global layer, DDSE highlights the differences among views by selectively retaining the discriminative features of each view and adjusting their weights; on the local layer, Grassmann manifolds are used to maintain the topological information among views and improve clustering adaptability. By learning a unified embedding from the Reproducing Kernel Hilbert Spaces, DDSE can capture high-order nonlinear dependencies among views and avoid information loss by generating a discrete indicator matrix. In addition, this paper derives an efficient optimization scheme to improve the performance of the proposed method. Multiple rounds of experiments on ten datasets verify the advantages of this method over other state-of-the-art methods.</p>