中国大学生就业

党的二十大明确提出了就业优先战略，对培养高质量人才和推动新兴产业发展作出了要求。这一战略布局不仅将人才培养与产业发展内在地关联起来，也为新时代就业服务的数字化转型确立了新的方向。但从现实来看，大学生就业仍面临总量压力处于高位与结构性供需错配持续加剧的双重挑战，传统就业服务模式在精准度与个性化方面显得较为滞后，这进一步加大了大学生的就业压力。本文围绕就业智能体所具备的自主感知、决策执行等特征，分析其如何打破传统就业服务信息单向传递的局限，以及如何解决信号传导不畅、人岗匹配效率低下等传统就业模式难题，促使就业服务向双向反馈的智能化赋能方向转型。在此基础上，文章先系统梳理就业智能体落地过程中面临的技术、伦理与资源挑战，再从数据治理、系统运行、生态构建三个层面提出可操作的实践路径，为高校就业服务数字化转型和改善大学生就业结构提供一定的学理支撑与行动参照。

The employment-first strategy articulated at the 20th National Congress of the Communist Party of China calls for cultivating high-quality professionals and accelerating the growth of emerging industries. This strategic blueprint inherently ties talent development to industrial upgrading and points employment services toward digital transformation. In practice,however,college graduates still face a twofold predicament:persistently high aggregate pressure and a widening structural mismatch between supply and demand. Conventional employment services,often lacking in precision and personalization,further add to the burden. Focusing on employment agents—with their capacity for autonomous perception,decision-making,and execution—this paper examines how they can break through the one-way information flow that has long characterized traditional services. It shows how such agents address weak signal transmission and inefficient person–job matching,nudging employment services toward intelligent,twoway feedback-driven support. The paper then systematically maps out the technical,ethical,and resource challenges that arise when putting employment agents into practice. On that basis,it proposes actionable pathways along three dimensions:data governance,system operation,and ecosystem building. The findings are intended to provide theoretical grounding and practical reference for the digital transformation of university employment services and for improving the employment structure of college graduates.