SUMMARY
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 potential correlations between the teaching evalua-tion results and the teacher’s age, gender, professional title, and academic qualification are analyzed with the Apriori algorithm, which is improved with the decision tree based on the research of the existing university teaching evaluation system. The back propagation (BP) neural network model is improved 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 improved association rule algorithm (ARA) 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 certain 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 ARA algorithm and DEA-BP algorithm based on the decision tree 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