Affiliations: School of Marxism, Shandong Women’s University, Jinan, China
Correspondence:
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Corresponding author. Yuxia Wang, School of Marxism, Shandong Women’s University, Jinan, China. E-mail: yuxiawang@tutanota.com.
Abstract: Ideological and political education plays an important role in supporting social talent input. However, the current evaluation effect of ideological and political education is difficult to quantify. Therefore, in order to improve the evaluation effect of ideological and political education, based on artificial intelligence algorithms, this study combines machine learning ideas and the current status of ideological and political education to build a fuzzy analytic hierarchy process model of the of ideological and political teaching quality based on machine learning and artificial intelligence. Moreover, this study uses a three-tier structure to build a model network structure, and based on the characteristics of fuzzy evaluation, this study uses the expert system to conduct data management, operation and control of model evaluation, and build a corresponding database to update the data in real time. In addition, in order to verify the effect of the model, this study sets simulation experiments to analyze the model performance. From the point of view of running effect and running speed, this research model meets the actual needs of the system, so it can be applied to the evaluation process of ideological and political teaching quality in colleges and universities.
Keywords: Machine learning, ideological and political education, teaching quality, fuzzy AHP model
