Internet of Things Based Korean Cross-Cultural Communication Interactive Talent Training Model under Curriculum, Ideology, and Politics

Abstract

With the development of society, the exchanges between various countries have become increasingly close, and this open communication mode also affects and changes the communication culture of each country. In the context of globalization, cross-cultural communication is increasing day by day, and cross-cultural communication ability has become a necessary quality for modern talents, so it is imperative to cultivate cross-cultural communication talents. However, the development level of informatization is insufficient, especially since the development of Internet of Things technology is lagging behind, the application level is low, and there are fracture characteristics in the application of each link. Information exchange cannot be effectively realized, information is incomplete, and asymmetry is obvious, and there is no effective information exchange platform. Following the trend of internationalization, scholars are paying more and more attention to the combination of cross-cultural communication and IoT technology. The combination of the two is to realize the real-time monitoring of personnel training. The acquisition and transmission of personnel training information is the primary problem to be solved in the realization of Korean cross-cultural communication. The wireless sensor network (WSN) has the characteristics of wide distribution of nodes, strong self-organization ability of routing, and good adaptability to dynamic changes in topology structure. WSN is used to solve this problem and is a good choice. Therefore, this paper used random forest algorithm and GBDT algorithm to apply WSN technology to the process of interactive talent training and established a network information service (NIS) system for interactive talent training. The experimental results have shown that the random forest algorithm and the GBDT algorithm can further simplify the input feature quantity and both can achieve good prediction results, while the GBDT model of the two models has relatively better prediction performance. The models obtained by the two methods meet the needs of detection parameter optimization, which realizes the real-time development of interactive talent training and realizes the intelligence and high efficiency of interactive talent training.