The improvement of teachers’ educational technology ability is one of the main methods to improve the management efficiency of colleges and universities in China, and the scientific evaluation of teachers’ ability is of great significance. In view of this, this study proposes an evaluation model of teachers’ educational technology ability based on the fuzzy clustering generalized regression neural network. Firstly, the comprehensive evaluation structure system of teachers’ educational technology ability is constructed, and then the prediction method of teachers’ ability based on fuzzy clustering algorithm is analysed. On this basis, the optimization prediction method of fuzzy clustering generalized regression neural network is proposed. Finally, the application effect of fuzzy clustering generalized regression neural network in the evaluation of teachers’ educational technology ability is analysed. The results show that the evaluation system of teachers’ educational technology ability proposed in this study is scientific and reasonable; fuzzy clustering generalized regression neural network model can better accurately predict the ability of teachers’ educational technology and can quickly realize global optimization. According to the fitness analysis results of the fuzzy clustering generalized regression neural network model, the model converges after the 20th iteration and the fitness value remains about 1.45. Therefore, the fuzzy clustering generalized regression neural network has stronger adaptability and has been optimized to a certain extent. The average evaluation accuracy of fuzzy clustering generalized regression neural network model is 98.44%, and the evaluation results of the model are better than other algorithms. It is hoped that this study can provide some reference value for the evaluation of teachers’ educational technology ability in colleges and universities in China.

中文翻译：

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基于模糊聚类广义回归神经网络的教师教育技术能力评价

教师教育技术能力的提升是提高我国高校管理效率的主要方法之一，对教师能力的科学评价具有重要意义。鉴于此，本研究提出一种基于模糊聚类广义回归神经网络的教师教育技术能力评价模型。首先构建了教师教育技术能力综合评价结构体系，然后分析了基于模糊聚类算法的教师能力预测方法。在此基础上，提出了模糊聚类广义回归神经网络的优化预测方法。最后分析了模糊聚类广义回归神经网络在教师教育技术能力评价中的应用效果。结果表明，本研究提出的教师教育技术能力评价体系科学合理；模糊聚类广义回归神经网络模型可以更好准确地预测教师教育技术能力，并能快速实现全局优化。根据模糊聚类广义回归神经网络模型的适应度分析结果，模型在第20次迭代后收敛，适应度值仍保持在1.45左右。因此，模糊聚类广义回归神经网络具有更强的适应性，并进行了一定程度的优化。模糊聚类广义回归神经网络模型的平均评价准确率为98.44%，模型的评价结果​​优于其他算法。希望本研究能够为我国高校教师教育技术能力评价提供一定的参考价值。