Higher education is of great significance in a country, both for the value of the industry and for the economy. First, we use the decision tree and AHP to establish an evaluation model based on the weight of criteria layer, which can evaluate the health degree of any country. Meanwhile, we conduct sensitivity analysis on the weight results. Then, we apply our model in the United States, Japan and South Korea for evaluation, compare the evaluation results of the TOPSIS evaluation model, further verify the correctness of the model, and obtain the evaluation scores of higher education health of the three countries. In the process of modeling, the correlation between various factors is emphasized, and quantifiable values are used as the link as far as possible. Through the relationship between each layer in the decision tree, we combine it with the weight to ensure research direction of the problem and the predicted index. This research method helps us to determine the best research direction in the evaluation of the higher education system with many influencing factors, and eliminates the interference of too many factors, so that our analysis and proposed policies are more targeted. Intuitive graphs also help us analyze the problem more effectively.

Tongzhenzhi Su. Research on Higher Education Evaluation Based on Decision Tree and AHP. International Journal of New Developments in Education (2022) Vol. 4, Issue 5: 32-36. https://doi.org/10.25236/IJNDE.2022.040507.

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