Abstract: With online teaching becoming one of the normalized teaching methods,people put forward higher quality teaching demands.Various online teaching platforms and the amount of educational resources on the Internet have greatly facilitated many learners.However,there are also some problems in educational resources such as uneven quality,lack of effective classification and integration,and mainly rely on manual sorting,which lead to people spending too much time and energy to search,screen and sort online educational resources.Considering the existing shortcomings of online education resources,this paper proposes an automatic classification method for online education resources based on natural language processing technology,and conduct experiments on the automated classification of eight English tense exercises,which are the key contents of middle school English grammar teaching.The experiment collects more than 90 000 English tense exercises both online and offline.After data cleaning,approximately 30 000 sentences are selected to construct a dataset,and a BERT fine-tuning text classification model is constructed.By training the model,automatic classification of the eight tenses is realized with an overall classification accuracy of 86.15%.And the recognition accuracy for the present tense is the highest,reaching 93.88%.To a certain extent,in terms of English tenses,the experimental result can meet the practical needs of automatic classification and organization of English education resources,intelligent correction and personalized push of exercises,intelligent Q&A.It provides a feasible idea and solution for improving the quality of online teaching and integrating online education resources.

Key words: Online teaching, Natural Language Processing, English tense classification