In order to provide timely and comprehensive interactive feedback on digital classroom teaching, a comprehensive evaluation of teaching feedback using BP neural network is proposed. Based on the characteristics of different evaluation subjects, the evaluation index system is chosen through a combination of questionnaire survey and expert evaluation method, and the main process plan for feedback analysis is provided. Then, the preprocessed dataset is divided into a training set and a testing set in a certain proportion. The indicator data generated during the learning process is imported into the trained neural network model for calculation, and the approximate performance score is acquired. Finally, a digital teaching evaluation model is established using MATLAB, and the specific analysis results of learning effectiveness are used to provide feedback on the teaching effectiveness of the course. The experimental results show that the accuracy of the proposed model reaches 95.98%, and the convergence curve is smooth, with overall performance superior to other similar models. Therefore, this plan is a good choice that can reasonably predict and effectively control teaching quality, effectively weakening the influence of human factors in determining indicator weights.