The analysis of the frontier issues of the English language teaching method in China is of great guidance for English language teaching. Based on the ontology model of English teaching domain, the knowledge map of English teaching in colleges and universities is constructed by fusing heterogeneous English subject data from multiple sources. Firstly, we obtain domain knowledge from relevant websites and existing documents through web crawlers and other techniques and clean the data based on BERT model; then, we use Word2Vec to judge the similarity between the research directions of characters and solve the entity alignment problem; based on the scientific knowledge map theory, we count the frequency of keywords in each year and analyze them to describe the association and union between keywords. It can explain the current situation and trend, rise and fall, disciplinary growth points, and breakthroughs of ELT. Through keyword analysis, the hot issues mainly revolve around ELT, English teaching, college English, grammar-translation method, curriculum reform, and so forth, to realize the quick query and resource statistics of ELT basic data, in order to promote the subsequent English discipline assessment work to be completed more efficiently.

This paper studies and analyzes three aspects: DL model, English talent training, and quality evaluation analysis, so as to get more rigorous and accurate quality evaluation results, and make relevant plans for future research directions. This paper focuses on model and method analysis to carry out experiments and analysis on three aspects: DL, personnel training, and quality evaluation. The experiment and inquiry of deep learning are divided into correct rate and loss. In 0-70 epoch, the highest training correct rate is 0.975, and with the increase of training times, the training correct rate is also increasing. According to the statistical investigation, 42.91% of English teachers are satisfied with their academic level, 38.48% with their oral English level, 38.89% with their teaching quality, 41.02% with their teaching methods, 40.29% with their teaching spirit, and 39.88% with their knowledge structure. At the same time, according to the statistics of students, graduates and teachers’ problems in English talent training, the largest proportion is the poor level of teaching resources, so we should improve the efficiency from the level of teaching resources. In order to improve the efficiency of quality evaluation method, this paper combines AF algorithm and BQ algorithm under deep learning. The error rate of algebraic algorithm is compared. Through six groups of sample data, it can be seen that the highest error rate of AF algorithm is 5.86% and the lowest is 0.92%, the highest error rate of BQ algorithm is 10.70% and the lowest is 1.10%, and the highest error rate of algebraic algorithm is 10.70% and the lowest error rate is 5%. In contrast, the error rate of AF algorithm is lower and more stable. Next, this paper compares and analyzes the performance of the AF algorithm, BQ algorithm, and algebraic algorithm. According to the experimental results, it can be seen that the AF algorithm is more accurate than the BQ algorithm and algebraic algorithm in accuracy, recall rate, F 1 , accuracy rate, etc. Therefore, it is more intuitive and accurate to evaluate the quality of English talents training through AF algorithm under deep learning.