Optimization of Classroom Teaching Quality Based on Multimedia Feature Extraction Technology

Abstract

In this article, the research of multimedia teaching video content feature extraction is carried out. According to the file structure, data type, and storage mechanism of the teaching video, a program is developed to automatically extract the structural features of the teaching video content, and a storage and retrieval database is established. The research results show that the accuracy rate of various videos compiled through genie 8.0 for teaching videos exceeds 94%. The recall rate of various videos exceeds 95%. The accuracy and recall of advertisements have reached 100%. Among the elements of teaching video content features, the number of graphs is the highest, followed by film clips, accounting for 14.18. Image, vivid, and interactive multimedia teaching video technology has greatly improved teaching effectiveness, promoting students to better understand and remember knowledge points. The research results provide theoretical data support for multimedia feature extraction to optimize classroom teaching quality.