Affiliations: School of Artificial Intelligence and Law, Shanghai University of Political Science and Law, Shanghai, China
Correspondence:
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Corresponding author. Ouyang Weimin, School of Artificial Intelligence and Law, Shanghai University of Political Science and Law, Shanghai, China. E-mail: datafax20284@163.com.
Abstract: In order to improve the evaluation effect of classroom education, this paper proposes the MFO intelligent optimization algorithm based on the idea of machine learning, and builds the classroom education effect evaluation model based on the MFO intelligent optimization algorithm. Moreover, this paper uses a logarithmic spiral to simulate the path of the moth to the flame and invert the pending parameters in the mathematical model, and adds vertical and horizontal algorithms and chaos operators on this basis. The crisscross algorithm allows different moth individuals and the same moth to perform cross calculations with different computing dimensions to increase the diversity of moth populations, so that moths in the search space can traverse the entire search space as much as possible to find a better solution. Moreover, in view of the problems of BP neural network such as low fitting accuracy, this paper applies the CCMFO algorithm to improve the BP neural network to form the CCMFO-BP algorithm, and improves the weight and threshold update process of the BP neural network to make the network operation more efficient and accurate. Finally, this paper designs experiments to analyze the performance of the model constructed in this paper. The research results show that the model constructed in this paper meets the expected requirements.
