In the process of deepening and developing the current higher education reform, people pay more and more attention to the research of college English education. The key to improve the college English education is to improve the quality of education, and learning evaluation is the key measure to improve the quality of education and training. This paper mainly studies the college English teaching quality evaluation system based on information fusion and optimized RBF neural network decision algorithm. This paper analyzes the main problems and complexity of creating an ideal learning quality evaluation system. On the basis of analyzing the advantages and disadvantages of the previous learning quality evaluation methods, this paper summarizes the existing learning quality evaluation methods and puts forward some suggestions according to the existing evaluation methods. A learning quality evaluation model based on RBF algorithm of neural network is proposed. RBF regularization network method, RBF neural network decision algorithm, and experimental investigation method are used to study the college English teaching quality evaluation system based on information fusion and optimization of RBF neural network decision algorithm. By innovating teaching methods and enriching teaching means, college students’ thirst for English knowledge can be aroused, and teachers’ teaching level can be improved. The results show that 50% of college students think that the level of college English teaching is average and needs to be improved. In the performance evaluation system of college English teaching quality based on information fusion and optimized RBF neural network decision algorithm, it is necessary to establish a learning evaluation system, monitor the learning quality in real time, find problems and improve them in time, and recognize the current situation of education.

中文翻译：

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基于信息融合和优化RBF神经网络决策算法的大学英语教学质量评价系统

在当前高等教育改革深化和发展的过程中，人们越来越重视大学英语教育的研究。提高大学英语教育质量的关键是提高教育质量，而学习评价是提高教育培训质量的关键措施。本文主要研究基于信息融合和优化RBF神经网络决策算法的大学英语教学质量评价系统。本文分析了创建理想的学习质量评价体系的主要问题和复杂性。在分析以往学习质量评价方法优缺点的基础上，本文总结了现有的学习质量评价方法，并根据现有的评价方法提出了一些建议。提出了一种基于神经网络RBF算法的学习质量评价模型。利用RBF正则化网络法、RBF神经网络决策算法和实验调查法，研究了基于信息融合和RBF神经网络决策算法优化的大学英语教学质量评价体系。通过创新教学方法，丰富教学手段，可以激发大学生对英语知识的渴求，提高教师的教学水平。结果显示，50%的大学生认为大学英语教学水平一般，有待提高。