针对数据分析与挖掘技术课程作业人工考核效率低、反馈同质化问题,研究基于Coze平台构建智能考核工作流,通过设计“本地预处理—云端分析”混合架构,融合知识库检索与大模型提示词工程技术,实现作业文本解析、代码评估及个性化反馈的全流程自动化。实践表明,该方案使单份作业批改耗时从120秒降至6秒（耗时降低95%）,在56名学生样本中评分结果与教师评价显著相关。研究成果为教育数字化转型提供了可跨学科迁移的智能考核范式。

To address the inefficiencies in manual grading and homogeneous feedback for assignments in the Data Analysis and Mining Technology course, this study constructs an intelligent assessment workflow based on the Coze platform. By designing a "local preprocessing-cloud analysis" hybrid architecture and integrating knowledge base retrieval with large language model prompt engineering, the research realizes end-to-end automation of assignment text parsing, code evaluation, and personalized feedback generation. Empirical results show that the solution reduces the grading time for a single assignment from 120 seconds to 6 seconds (a 95% time reduction), and the automated scores demonstrate a significant correlation with instructor evaluations in a sample of 56 students. The findings provide a cross-disciplinary adaptable paradigm for intelligent assessment in the context of educational digital transformation.