This paper explores the potential applications of Large Language Models (LLMs) in college English teaching and aims to provide research references and practical insights. Five cross-scenarios of LLMs are demonstrated, namely, the optimization of vocabulary, grammar, reading, speaking, and writing teaching links, using Intermediate College English for New Era (Book 2) as a case study. It is found that LLMs enable dynamic categorization of thematic vocabulary and multimodal visualization to construct semantic fields, thereby reinforcing students’ associative vocabulary learning; through error-generation mechanisms and comparative text analysis, they facilitate targeted instruction of grammatical rules with hierarchical training adaptations; by implementing sentiment scale segmentation and implicit narrative tracking, they foster the development of critical thinking and empathetic reasoning competencies; when integrated with speech recognition technology, they permit objective evaluation of oral proficiency metrics; their generative revision mechanisms effectively enhance textual coherence in writing outputs. Leveraging deep semantic comprehension and generative capacities, LLMs significantly optimize the precision and efficiency of language knowledge acquisition and skill development in educational contexts.