Abstract: With the deep penetration and large-scale application of large artificial-intelligence models to key vertical fields such as medical care, finance, autonomous driving, and industrial manufacturing, the data annotation industry is transforming from traditional labor-intensive to high-end task with “high technology content, high knowledge density, and high value application”, and its importance of supporting the AI industry ecology is increasing. However, the talent gap of high-end data annotation in China has reached a million scale. This contradiction between supply and demand has become a bottleneck restricting the implementation effect of “artificial intelligence+” action and the development of new quality productive forces. Based on the key insights and typical practice cases from speakers on special forum of CCF YOCSEF Baoding about “Millions High-end Data Annotator Gap: Whose fault How to fix it ”, this article firstly systematically defines the connotation and three-dimensional capability demand system of high-end data annotation, and then analyzes the mechanism of gap formation from three aspects of industrial ecological inertia, education and training system lag, and social cognitive deviation. Finally, a four-party collaborative breakthrough path of “industrial standard unification + education model innovation + technology tool empowerment + policy ecological collaboration” is proposed. The training of high-end data annotation talents needs to break disciplinary barriers and professional cognitive bias, and build a “quality-oriented” interdisciplinary training system through collaboration between schools and enterprises, so as to provide solid talent support for high-quality application of large models in vertical fields and help China’s artificial intelligence industry build core advantages in global competition.