Spatio-temporal Feature Extraction and Knowledge Graph Construction Method for Foreign Language Teaching Video Content Understanding

The development of online education has continuously promoted the level of intelligent analysis of foreign language teaching videos.This paper proposes a content comprehension analysis method for foreign language teaching videos that integrates spatio-temporal residual attention network (STRAN) and improved text rank algorithm (TextRank).The spatio-temporal features in the video are extracted by three-dimensional residual network (3DResNet), and the attention mechanism is combined to optimize the recognition accuracy of students' classroom expressions.Improved TextRank algorithm is utilized to filter the keywords of teaching focus and integrate multisource data and storage technology to construct a knowledge graph of foreign language teaching.The results show that the recognition rate of all 6 kinds of student expressions of STRAN is more than 0.930.The 3 keyword extraction performance indexes of Improved TextRank are all greater than 90.00% in different numbers of keyword extraction.Comprehensively applying the method of this paper to assist teaching, the students' concentration and excitement expression scores are more than 90 points, and the scores of confused, distracted, nervous, and sleepy expressions are around 70-85 points.