Construction of a Basic Japanese Teaching Resource Base Based on a Deep Neural Network under a Big Data Environment

Abstract

A challenge for education and teaching in universities is posed by "Internet plus," which has made numerous educational resources at universities richer and more accessible. The development of a professional Japanese teaching resource base should be centered on the needs and characteristics of Japanese teaching in universities, as well as establish and enhance the mechanism for resource base construction. All forms of instructional resources should also continuously be updated and improved in order to realize the diversified, systematic, open, and long-term development of Japanese instructional resources. In light of the current state of the information technology industry's rapid expansion, this essay examines a few issues with the building of a Japanese teaching resource database. A fundamental Japanese teaching resource database built on DNN was created as a result. The CNN technology is used in this study to create the Arduino device identification application. Utilizing gadgets in the learning process, learners can obtain learning resources using the Arduino device identification program before engaging in learning activities. The experimental findings also demonstrate that the precision rate and recall rate of the Japanese teaching resource database system developed in this study may achieve about 93 and 94 percent, respectively. Its performance is better than the conventional teaching resource system, and it can offer top-notch teaching resources for teaching fundamental Japanese.