结合研究生双导师制在当前高校教学中的实施情况，分析现有研究生培养体系中校内外导师协作沟通不畅、师生精确匹配困难、导师激励机制不足、评价指标构建不合理等问题，借助智能基座与虚拟教研室的教学资源，提出针对性的双导师制下的师生匹配优化方案以及可量化的导师评价指标构建方案，具体阐述如何构建物质、学术、职业发展支持相结合的多维激励体系，强化校内外导师协同培养，助力专业硕士综合能力的系统化提升。以大数据技术与人工智能前沿热点课程为例，验证所提优化方案可以达到更好的师生匹配效果。

Combining the implementation of the dual-tutor system of postgraduate students in current higher education, existing problems in postgraduate education, including ineffective collaboration between internal and external tutors, difficulties in accurate tutor and student matching, insufficient incentive mechanisms, and the lack of rational evaluation metrics are analyzed. Leveraging intelligent base and the teaching resources of virtual instructional research office, targeted optimization strategies for student and tutor matching under the dual-tutor system, as well as a framework for constructing quantified tutor evaluation metrics are proposed. Furthermore, it elaborates on building a multi-dimensional incentive system that integrates material, academic, and career development support, thereby strengthening collaboration between internal and external tutors and promoting the systematic improvement of the comprehensive capabilities of postgraduates. Taking frontier course in big data and artificial intelligence technologies as case studies, the proposed optimization strategies are validated to yield more effective student and tutor matching outcomes.