Abstract: With the rapid development of high-performance computing technologies, the complexity and scale of scientific computing problems have significantly increased. As a critical input for scientific computing, mesh generation has emerged as an important research direction in high-performance computing. To address challenges such as low computational efficiency and complex human-machine interaction in large-scale mesh generation, intelligent mesh generation methods have attracted increasing attention. However, achieving a deep integration of high-performance computing and artificial intelligence in mesh generation remains a critical challenge. To overcome these challenges, an intelligent parallel mesh generation framework based on unsupervised learning is proposed, enabling the efficient generation of large-scale multi-block structured meshes. The framework integrates a block partitioning method based on cross-fields, a Fourier Kolmogorov-Arnold network model, and a mesh alignment technique. It employs an unsupervised learning approach to adaptively learn high-quality mesh partitioning rules, effectively overcoming the limitations of existing intelligent methods in handling complex geometries. Additionally, a novel parallel transfer learning mechanism is introduced, significantly enhancing model convergence efficiency by transferring task similarity features across different processes. Experimental results demonstrate that the proposed framework achieves up to a 10-fold improvement in efficiency over traditional mesh generation methods for hundred-million-scale tasks. Furthermore, it outperforms existing intelligent mesh generation methods across five different quality metrics, achieving state-of-the-art performance.