为应对中医药信息化发展对复合型人才的迫切需求，并探究高校人才培养与市场需求的潜在偏差，提出一种基于文本挖掘与K-Means聚类算法的人才供需匹配度分析框架。首先，利用DrissionPage框架编写数据采集脚本，自动爬取主流招聘网站数据，利用K-Means聚类算法构建岗位技能需求画像；其次，通过分析全国中医药院校信息工程相关专业课程，构建学生技能供给画像；最后，采用Jaccard指数计算二者的匹配度，生成可视化匹配矩阵，根据匹配度与岗位画像，从课程改革实施路径、不同层次院校适配方案、技能缺口填补培训等方面，为中医药院校信息工程类专业提供差异化的人才培养优化建议。

To meet the urgent demand for composite talents in the development of traditional Chinese medicine informationization and explore the potential deviation between talent cultivation in universities and market demand, a talent supply and demand matching analysis framework based on text mining and K-Means clustering is proposed. The DrissionPage framework is used to write data collection scripts, automate the crawling of mainstream recruitment website data, and use the K-Means algorithm to construct job skill demand portraits. By analyzing the courses related to information engineering in traditional Chinese medicine colleges and universities across the country, we aim to construct a skill supply profile for students. Using Jaccard index to calculate the matching degree between the two, a visual matching matrix is generated. Finally, based on the matching degree and job profile, differentiated talent training optimization suggestions are provided for information engineering majors in traditional Chinese medicine colleges from the aspects of curriculum reform implementation path, adaptation plans for different levels of colleges, and skill gap filling training.