Utilizing Embedded Machine Learning Technology for Constructing Educational Cultural Identity Index Systems in Higher Education: A Biotechnological Approach

Abstract

In this paper, we explore the construction of an educational, cultural identity index system in colleges and universities using embedded machine learning technology. Initially, we discuss the research on ideological education models in higher education institutions, focusing on the concept of green development. This concept is utilized to innovate and develop green and ecological ideological education modes, aiming to create a healthy and sustainable environment for educational activities. Next, under the framework of environmental protection and ecological sustainability, we propose a green cycle system for the management of ideological and political education. This includes the development of an evaluation system for the ecosystem of Civic and Political Education, grounded in ecological theory. The evaluation system comprises four primary indicators: decomposer, producer, consumer, and external environment, along with six secondary indicators and nineteen tertiary indicators. We analyze the weights of these secondary and tertiary indicators to determine their influence on the overall system. The results indicate that the weighting coefficient for ideological education is the highest among the factors. In comparison, secondary factors such as theoretical courses and practical education, along with the condition guarantee and nurturing environment, have a smaller impact on college students' ideological and political education. This study underscores the potential of machine learning and biotechnology to enhance the construction and evaluation of educational, cultural identity systems in higher education.