China’s economy has become more globalized in the twenty-first century. English has become the primary means of communication and trade between people and other countries as the economy has rapidly developed. As a result, colleges and universities have increased the English proficiency and teaching quality criteria for college students. The success of college instructors’ English instruction is judged using teaching quality as a primary metric. The procedure of assessing the quality of English instruction is complicated. Using appropriate assessment indexes and procedures, a scientific and sensible evaluation system for English education should be developed. This study uses the AHP and fuzzy decision tree algorithms to investigate the quality of teaching (preferably English) and gives teaching evaluation data to university administrators, which is useful to enhance the quality of teaching (preferably English) in colleges. First, a hierarchical analysis-based evaluation index system (EIS) for English teaching is established, followed by a detailed description of the decision tree algorithm’s calculation process and the application of the fuzzy decision tree algorithm to the evaluation of teaching (preferably English) quality. Finally, the practical application effect of the algorithm is tested. The results show that the accuracy of this algorithm is higher than other algorithms, and it is helpful to improve the efficiency of evaluating English teaching quality.

Traditional English teaching cannot make effective use of various resources, and the scheduling ability is poor. People cannot accurately obtain the information in the English textbook text in the learning process, resulting in some people who cannot better learn and master the English language. For this problem, this study adopts deep learning algorithm and establishes an English teaching text algorithm based on association semantic rules to mine the features between sentences and phrases in the text provided by English teachers. The proposed algorithm completes the feature extraction of the English teaching text and also analyzes the association analysis between semantics in English teaching text. In fact, its essence is to get English teaching association rules on the basis of information theory. By combining with semantic similarity information, English teaching text can be accurately detected and identified. The simulation results show that the proposed algorithm can accurately extract English teaching text information, and the accuracy and convergence speed during extraction are higher than other competing algorithms.