With the rapid development of generative artificial intelligence technologies, represented by large language models, university-level computer science education is undergoing a critical transition—from knowledge-based instruction to competency-oriented teaching. A postgraduate student competency evaluation model can serve as a framework to organize and guide both teaching and research activities at the postgraduate level. A number of relevant research efforts have already been conducted in this area. Graduate education plays a vital role not only as a continuation and enhancement of undergraduate education but also as essential preparation for future research endeavors. An analysis of the acceptance of competency evaluation models refers to the assessment of how various stakeholders perceive the importance of different components within the model. Investigating the degree of acceptance among diverse groups—such as current undergraduate students, current postgraduate students, graduates with less than three years of work experience, and those with more than three years of work experience—can offer valuable insights for improving and optimizing postgraduate education and training practices.