Semantic Matching Optimization of Foreign Language Courses and Industry Requirements Based on the BERT Model

As globalization and technological advancements continue to accelerate, the demand for foreign language education is expanding, particularly in aligning undergraduate foreign language curricula with the evolving needs of the industry. A significant challenge exists in the current educational model, where traditional course design fails to adapt to the dynamic requirements of the job market, leading to a mismatch between the knowledge imparted in educational settings and the skills demanded by employers. To address this issue, this paper proposes a semantic matching approach using the BERT (Bidirectional Encoder Representations from Transformers) model, aiming to enhance the alignment between foreign language course content and industry needs. By constructing a comprehensive corpus consisting of both industry demand data and course content, the paper leverages the BERT model’s advanced contextual understanding capabilities to semantically match the two datasets, significantly improving the accuracy of the matching process and ensuring that foreign language courses are better tailored to meet industry-specific language requirements.