English is an important international language. Whether it is international economic or international political communication, it is inseparable from English. English teaching is mainly to impart the basic knowledge of English as well as the knowledge of audiovisual knowledge. However, the traditional English teaching mode often ignores the expression of English emotion and the background of English. The traditional English teaching mode often uses textbooks and other forms to impart knowledge, which can no longer meet the English teaching in today’s era. Computer-aided systems have become an important technology in the field of teaching today. Multimedia technology can also show English knowledge to students in the form of video or audio. This will increase students’ interest in learning English. This research will use computer platform and multimedia technology to build a new English teaching platform. It also uses the ConvLSTM algorithm and the CF algorithm to implement the active recommendation function of English knowledge. The research results show that multimedia technology can achieve the purpose of collecting video and audio information in the process of English teaching. The CF algorithm has a specific high similarity index in recommending English knowledge. The ConvLSTM method can also better predict the characteristics of English grammar and English emotion in the English teaching process. This can also show that the multimedia-assisted English teaching mode has a certain use value for different English teaching scenarios.

English language teaching (ELT) has become an essential and indispensable part of primary education in today’s increasingly frequent international exchanges, and MOOC, as a massive online teaching model integrating content and learning support services, is leading a pedagogical transformation, providing a historic opportunity to build an open sharing platform for ELT. However, developing an open sharing platform around MOOC networks is still challenging, especially for English courses, where various course designs significantly increase the difficulty of tracking students’ learning status. Therefore, we introduce RNN-based sequence-to-sequence knowledge tracing models as the software foundation of the shared platform. The transformer model is further chosen to simulate students’ historical learning trajectories to solve the problem of long-term dependency in traditional models. The research results have important theoretical and practical implications for building an open sharing platform for ELT courses.