No abstract

With economic globalization and the informatization of social life, English has become a language extensively used around the globe. The research on the evaluation of English teaching quality is of crucial practical significance and theoretical value. Hence, student evaluation form for the college English classroom teaching quality that has been evaluated and has good reliability is used in this paper to expound the principles of the triangular fuzzy number evaluation method and the range method based on the analytic hierarchy process (AHP). Examples are used to analyze the differences between the two methods as well as their advantages and disadvantages. The comprehensive evaluation result of the teaching quality evaluation of a teacher by using the range method is excellent (85.500 points), and the comprehensive evaluation result by using the triangular fuzzy number method based on the analytic hierarchy process is good (64.818 points). The results show that the range method is better for the comprehensive evaluation of teachers' teaching quality. The statistical analysis results suggest that there are differences between the two methods in the evaluation of the classroom teaching quality in colleges and universities (t = 11.197, P < 0.0001). In the evaluation of the teaching quality by using the triangular fuzzy number evaluation method based on the analytic hierarchy process, the non-linear and fuzzy features of evaluation factors are taken into consideration comprehensively. The weight values of various evaluation indexes are calculated scientifically. The qualitative and quantitative evaluations are combined to obtain the evaluation result of the classroom teaching quality of teachers more objectively, which is of great significance in the practical teaching quality evaluation process.

On the background of “Internet +” era, the domestic higher education is showing the trend of artificial intelligence. The reliability and scientificity of computer intelligent evaluation are further carried out, and the mode of intelligent evaluation and data analysis in optimizing the precise teaching of English writing is explored, which can lay a foundation for the large-scale use of the technology. Based on data-driven theory, this study further analyzed the role of AI in promoting in-depth learning by comparing AI writing review model with manual review model.

In order to improve the convenience of English education management and improve the quality of teaching, this paper proposes the application of English education information management system based on convolution neural network classification algorithm. Specifically, it relies on spark cloud technology and data mining technology of cash to build an English education information management system. Secondly, in the educational information management platform, convolution neural network classification algorithm is used to mine useful information. Convolution network mainly combines the emotional words, parts of speech, degree adverbs, negatives, punctuation and other word features that affect the emotional tendency of the text to form an extended text feature. Then, the word vector feature and the extended text feature are used as two input channels of convolution neural network, and the dynamic K-max pooling strategy is adopted to improve the ability of feature extraction of the model. The experiment in the English education information management system shows that the classification performance of the algorithm in this paper is not only higher than that of the single channel convolution neural network algorithm, but also has some advantages compared with some representative algorithms. In addition, experiments verify the effectiveness of the educational information management system.

In this study, the questionnaire survey, diversified classroom teaching, and multivariate evaluation are carried out on college students of non-English majors based on the Roche multiway tree clustering method. The SPSS statistical software is used for statistical analysis. The results show that Roche multiway tree cluster distribution of non-English major college students is at a medium level. There is no significant correlation between the comprehensive English performance and the overall Roche multiway tree clustering, and there is a low positive correlation between Roche multiway tree clustering and English listening. Studies have suggested that in comprehensive English classroom teaching, teachers should pay attention to the weak intelligence of students, teach students according to their intelligence difference, build a diversified classroom teaching evaluation model, and promote the all-round development of student intelligence.