Resource Collection Algorithm for Entrepreneurship and Employment Education in Universities Based on Data Mining

Abstract

Graduate unemployment is one of the serious challenges in China, including the graduates of a large number of public and private higher education institutions. The collection of entrepreneurial employment education resources in colleges and universities is a basic project and a key link to promote the rapid development of education informatization. Data mining has various applications in different fields such as health care, smart agriculture, smart cities, smart businesses, and education, but is playing a vital role in the field of education and businesses. The applications of data mining provide new technical tools and development directions to realize the common construction, sharing, and collection of entrepreneurial employment education resources in colleges and universities. The closed nature of teaching resources within colleges and universities leads to the inability of external search engines to search them, which hinders the search and access of teachers and students and seriously affects the smooth implementation of current innovation and entrepreneurship employment work. Aiming at the real demand of entrepreneurial employment education resource collection in colleges and universities and the characteristics of on-campus resources, this study proposes a data mining-based algorithm for entrepreneurial employment education resource collection in colleges and universities. The algorithm obtains entrepreneurial employment demands from the academic affairs system, collects on-campus online teaching resources through internal crawlers, and provides services for teachers, students, and employees through online teaching resource collection drive subalgorithm and quick recommendation subalgorithm. We also compared the proposed model with the CLR model. The case analysis and performance experiments show that the proposed algorithm has a good resource mining effect, high user satisfaction, and high recommendation efficiency, occupies fewer system resources, and shows high performance as compared to the CLR model.