Intelligent Decision System of Higher Educational Resource Data Under Artificial Intelligence Technology

Abstract

It aims to apply the neural network algorithm to the mining of educational resource data and provide new ideas for the intelligent development of teaching evaluation. The Apriori algorithm is modified with the decision tree based on the research of existing university teaching evaluation system. The modified Apriori algorithm is applied to analyze the correlations of the teaching evaluation results to the teacher’s age, gender, professional title, and academic qualification. The back propagation (BP) neural network model is improved as the DEA-BP based on the differential evolution algorithm (DEA). The DEA-BP model is applied to the prediction of teaching evaluation results for analysis. The results show that the execution time of the modified Apriori algorithm is significantly better than that of other models. In addition, the teacher’s age (40 - 50 years old or 50 - 60 years old), gender (female), professional title (senior or deputy senior), and academic qualifications (undergraduate or master) have reliable correlation with the teaching evaluation results (excellent). When the DEA-BP algorithm is adopted to predict the teaching evaluation results, the average absolute error (1.05%) and the relative accuracy rate (95.44%) between its prediction value and the true value are optimal. Therefore, the Apriori algorithm and DEA-BP algorithm can intelligently extract the potential laws and knowledge in the teaching evaluation data, and provide support for teaching evaluation decisions. Thus, it exerts the role of promotion in the mining of educational resource data in universities and the intelligent development of decision-making systems.