English Interpretation Learning System Combining Cognitive Ability and Collaborative Filtering Algorithm

Abstract

In the context of globalization, English interpretation is the language hub for political and economic exchanges in different countries. English interpretation is a technology different from the English translation, which requires translation accuracy and real-time. Traditional English interpreting learning methods can only learn English grammar and some English language and cultural information. At the same time, traditional English interpreting learning methods are inefficient and cumbersome tasks. English interpreting participants also need to interpret accurately in real-time based on the speaker’s behavioral information and the cultural information contained in English. This puts forward more requirements for the learning of English interpreting learners. This research uses the collaborative filtering (CF) algorithm and the algorithm based on ConvLSTM cognitive ability to study the three characteristics of English grammar, behavioral information, and English cultural information in the English interpreting learning system. The CF algorithm can recommend effective English knowledge for English interpreting learners, and the ConvLSTM algorithm can interpret the effectiveness of this recommended knowledge. The research results show that the CF algorithm and the ConvLSTM algorithm have good performance in recommending and predicting the grammar, behavioral information, and cultural characteristics of the English interpreting learning system. The largest similarity index reached 0.97, and the smallest similarity index also reached 0.93. ConvLSTM can better predict the changing trend and data value size of English interpretation-related feature data, and the largest prediction error is only 2.48%.