The introduction of the ecological perspective into the research of entrepreneurship education is an important transformation of the way of thinking. Using the thinking forms such as integrity, diversity, and openness, it analyzes the ecological role relationship of the various elements of entrepreneurship education itself, and the relationship between the entrepreneurship education system and the external social ecology. The relationship between the system and the influence of various elements on the effect of entrepreneurship education is clarified to ensure that entrepreneurship education achieves practical results. From the perspective of ecology, based on the research on entrepreneurship education in colleges and universities, on the basis of ecological cognition of entrepreneurship education, this research attempts to solve many problems in the current implementation of entrepreneurship education in colleges and universities through the theoretical construction of an ideal college entrepreneurship education ecosystem. The problem of ecological imbalance can better serve the practice of entrepreneurship education in colleges and universities. At the same time, theoretically expand the horizons of entrepreneurship education research in colleges and universities, and further promote the “conditional” integration of ecological thinking and entrepreneurship education. This paper introduces the random matrix theory and introduces the related theory of Monte Carlo method. The weight assignment of the evaluation index system is realized, which lays the foundation for the quantitative evaluation. The weight coefficients of the indicators at all levels are determined by the analytic hierarchy process, and the importance of the indicators is judged by the weight coefficients, so as to realize the scientific quantitative evaluation of the innovation and entrepreneurship education ecosystem. At the same time, the scientificity of the evaluation index system is verified by means of the method of reliability and validity test. The conclusions of this study theoretically solve the main problems existing in the evaluation of the current innovation and entrepreneurship education ecosystem. By building a complete evaluation index system for the innovation and entrepreneurship education ecosystem in colleges and universities, it helps researchers and users to clarify the connotation and elements of the innovation and entrepreneurship education ecosystem in colleges and universities, and fully taps the “hidden” elements of evaluation indicators, making the evaluation more comprehensive.
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.
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