基于大数据的智能制造岗位与技能需求研究

在不违反相关协议准则的情况下,通过爬虫技术获取智能制造岗位数据,并对其进行清洗与脱敏处理. 应用Jieba中文分词工具、K-means聚类算法与隐含狄利克雷分布（LDA）模型,将岗位名称分为6类,将技能集分为8类. 最后,构建需求矩阵并归一化处理,得到各技能集对岗位簇的重要程度,为专业选择、课程建设与从业人员发展提供参考.

Research on intelligent manufacturing positions and skill requirements based on big data

Without violating relevant protocol guidelines, the intelligent manufacturing job data was obtained by crawler technology, which was cleaned and desensitized in this paper. By Jieba Chinese text segmentation, as well as clustering algorithms such as K-means clustering algorithm and latent Dirichlet allocation （LDA） model, job titles were categorized into six clusters. Besides, skills were classified into eight clusters. Finally, a demand matrix was constructed and normalized, revealing the importance of each skill set to job clusters. The research was able to provide reference of choosing majors, curriculum development and the professional development of practitioners.