Research on intelligent matching of students’ learning ability and healthcare job market demand based on industrial engineering expertise graph

In China, there is a structural mismatch between the job market and student employment, characterized by "unfilled jobs" and "unqualified candidates," particularly between the industrial engineering (IE) profession and the healthcare services sector. Expertise graphs are designed to identify the logical connections between academic disciplines and job market needs, linking students' knowledge and skills with job requirements. This approach provides a systematic and visual alignment between students' learning outcomes and job market demands, addressing the mismatch. However, current expertise graphs have not effectively captured the intrinsic connection between students' learning abilities and healthcare job market demands. Additionally, research on intelligent matching and the construction of knowledge graphs for IE remains limited. This study aims to bridge this gap and alleviate the structural mismatch between the healthcare job market and student employment in China. First, an expertise graph for IE is developed, covering both expertise and healthcare job requirements. A multi-layer fusion information extraction model, combining BERT, BiLSTM, and GCN, is then proposed for knowledge extraction. An employment matching algorithm is introduced to extract healthcare job titles and requirements from the knowledge graph, calculate similarity with students' overall ability scores, and recommend suitable positions. Finally, a case study demonstrates that the algorithm accurately analyzes students' ability scores and successfully matches IE majors with relevant healthcare job positions, validating its effectiveness. This study aims to mitigate the structural mismatch between the healthcare job market and student employment, providing high-quality IE talent to medical services, which has significant scientific and practical value.