Optimization, Integration, and Open Sharing of Business English Course Resources Based on Embedded Neural Networks

In the context of globalization, the construction and optimization of core courses in Business English have become a key link in the development of Business English majors in universities. Faced with the problems of scattered traditional curriculum resources and low efficiency of use, as well as the challenges of the explosive growth of online learning resources in the era of “mobile Internet plus+education”. How to effectively integrate and utilize business English course resources, build a systematic, rich, and efficient learning system, has become an important issue in improving the teaching quality and student learning experience of business English majors. This paper proposes the use of embedded neural network technology for deep mining and intelligent integration of business English course resources. By constructing a neural network model to analyze the characteristics of course resources such as content, difficulty, and applicable objects, automatic classification, labeling, and correlation analysis of resources can be achieved, thereby constructing a clearly structured, rich content, and closely connected business English course resource library. In addition, combined with personalized recommendation algorithms, the most suitable course resources are recommended to learners based on their learning behavior, interest preferences, and ability levels, thereby improving learning efficiency and satisfaction.