Significant efforts have been focused on enhancing the utilization of multiple node features and topological structures in multi-view graph learning through explicit model-based and implicit deep-based methodologies. The former excel in embedding prior knowledge in learning graphs, thereby offering the theory-level interpretability but limiting in the application-level flexibility because of manual parameter selection. In contrast, the latter leverage automatic differentiation mechanisms for learning graphs, providing greater application-level flexibility but suffering from reduced the theory-level interpretability due to their opaque nature. Motivated by these observations, we propose an interpretable deep unfolding network for mutual-benefit multi-view graph learning, aiming to combine the strengths of both approaches. First, we employ the ADMM optimizer to effectively solve the multi-view graph learning model with sparse and low-rank constraints, integrating this iterative solution of mathematical support into the construction of explicit-level deep unfolding networks, thereby enhancing the theory-level interpretability. Second, we convert certain conditions into implicit-level losses and leverage automatic differentiation capabilities to update parameters, thereby reducing the necessity for manual parameter tuning and enhancing the application-level flexibility. Through this collaboration, we optimize multi-view learning for a graph representation that balances interpretability and flexibility. Empirical evaluations across six diverse datasets demonstrate the effectiveness and superiority of the proposed method compared to state-of-the-art approaches.