<p>To mitigate subjective biases and guarantee the objectivity and fairness of evaluation criteria, while enhancing the quality of fuzzy comprehensive assessments for higher education, a study is conducted on a fuzzy comprehensive evaluation approach for higher education quality, leveraging a multi-layer BP neural network. Firstly, select evaluation indicators for the quality of higher education based on the principles of scientificity, comprehensiveness, and test ability; Then, to address the issue of excessive indicators, the fuzzy AHP method is introduced to structurally process and rank the indicators; Finally, the processed evaluation indicators are input into a multi-layer BP neural network for educational quality evaluation. Through forward propagation and error back propagation, the mapping relationship between the input indicators and the evaluation results of higher education quality is automatically learned and established to achieve teaching quality evaluation. The experimental results show that the R2 of the multi-layer BP neural network is 0.95, and the actual level is completely consistent with the level corresponding to the expected output. The test output is also highly close to the expected output. The AUC value of the area under the ROC curve should be 0.92, which can effectively achieve comprehensive evaluation of the quality of higher education.</p>