Empirical analysis of integrated teaching mode of international trade based on practical training data detection and neural network

Abstract

With the continuous development of China's foreign trade, the foreign trade industry requires universities to train more application-oriented talents with specific theoretical basis and professional knowledge. However, on the one hand, the gap between international trade professionals within enterprises is widening; on the other hand, many international trade graduates are not engaged in the foreign trade industry because their professional skills are not recognized, resulting in a waste of teaching resources. The root of this dilemma lies in the serious disconnection between the traditional training mode of international trade professionals and the international development background of Chinese foreign trade enterprises. This paper focuses on neural network technology and then analyzes the integrated teaching mode of international trade. The integrated teaching resources studied in this paper come from various sources, mainly including traditional textbooks, courseware and other resources, as well as self-made MOOC courses and other MOOC platform course resources and other online teaching resources. In this paper, the framework of the integrated teaching model is mainly designed from three aspects: pre-analysis, curriculum design, assessment and evaluation. It can be found that the following practice on integrated teaching mode is very successful in MOOC, which improves students' self-study ability and also cultivates their ability to cooperate and communicate. This proves that the integrated teaching model of international trade in this paper is effective. Through the research and discussion of neural network technology, this paper applies it to the integrated teaching mode, which provides a more powerful technical guarantee for the training of international trade talents.