Ideological and political education system based on emotion analysis in large-scale online education

Abstract

The amount of data on the Internet is showing an explosive growth trend. The recommendation system can help users find the resources they need from a large number of videos, which has become an urgent problem to improve the effectiveness of online education. With the rapid development of Internet of Things technology, the timeliness of information collection and processing has been further improved. This paper constructs a multi-information fusion sequence recommendation system for ideological and political online education. Specifically, the facial video information is collected by the camera, and these videos are delivered to the server. We introduce gate recurrent unit (GRU) to analyze the human expression state. By exploiting human emotion during online ideological and political video learning, we design a novel sequence recommendation system in which human emotional states and related videos are fused based on the multi-head attention mechanism. Experimental results show that video recommendation performance can be effectively improved by introducing emotional information.