<p>Block-structured mesh generation offers significant advantages in numerical computation and simulation, yet conventional methods often rely on manual intervention, struggling to balance automation with high-quality output. To address this, an automated block-structured mesh generation framework that integrates deep reinforcement learning and optimal conformal mapping techniques is proposed in this paper. This framework utilizes the triangulation of the geometric model as input and operates in the following four steps. Firstly, surface triangular meshes are mapped to planar parametric domains using the Ricci flow algorithm. Secondly, isocontours are extracted based on density variation before and after conformal mapping to guide partitioning. Thirdly, a reinforcement learning decision framework is constructed to formulate mesh generation as a sequential decision-making process, where topological template selection and singularity placement are optimized through reward functions. Finally, surface-structured meshes are generated through mesh smoothing and inverse conformal mapping. Experimental results demonstrate that the proposed method outperforms existing methods in both generation efficiency and mesh quality, providing an intelligent new solution for automated mesh generation in CAD/CAE applications.</p>