In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

中文翻译：

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基于递阶PSO-BP神经网络的在线教学质量评估模型的优化

在教学质量评估中，针对BP神经网络收敛速度慢，容易陷入局部最优的缺点，提出了一种基于层次分析法和粒子群优化BP神经网络的在线教学质量评估模型。 BP）。首先通过层次分析法建立在线教学质量评价体系，确定在线教学质量评价体系中各个子系统和各个指标的权重，然后结合实际经验，根据该指标构建风险指标。安全规定。通过BP神经网络建立回归模型，并通过粒子群算法对模型的权重和阈值进行优化。基于BP神经网络的在线教学质量评价模型，不断调整模型的参数，选择合适的功能，优化了用于神经网络训练学习过程的粒子群算法。通过信度和效度检验验证了问卷的科学性。根据评分结果，结合在线课程质量评价指标体系中各项指标的权重系数，得出影响在线课程质量的关键因素。基于调查数据，使用描述性统计数据，方差分析和Pearson相关系数方法来验证研究假设并获得有价值的经验结果。通过将模型与标准BP模型进行比较，