Network Autonomous Learning and Student Training Education System under Human-computer Interaction Environment

Abstract

With the popularization of the Internet, more and more information can be inquired through the Internet, which also corresponds to the Chinese proverb, “You can know the world without going out at home.” With the development of human-computer interaction systems, online learning has gradually become a mainstream. Self-directed learning is to determine learning goals, determine learning content, determine learning process, select appropriate learning methods, master learning process, and self-evaluate. The study of Japanese includes a large amount of relevant knowledge, including language, literature, history, politics, economy, diplomacy, social culture, etc., and has certain professional skills and skills. Therefore, this paper proposes a research on the network autonomous learning and student training education system in the environment of human-computer interaction. It firstly introduces the human-computer interaction system and its structure. Then based on the neural network algorithm to analyze the network self-learning algorithm, this paper puts forward corresponding suggestions for improvement, and conducts a questionnaire survey on Japanese majors in a university to analyze their self-learning ability. Finally, it analyzes its education system and puts forward some opinions. The results of the experiment showed that 54% of the people believed that they could learn autonomously even when the teacher was away. 22% said no, and 24% said they were not sure. This is enough to show that the school’s students are not independent enough, and most students seldom study independently.