Enhancing Language Comprehension and Acquisition Efficiency in English Intercultural Communication through Intelligent Semantic Analysis

Abstract

The traditional mode of teaching English intercultural communication suffers from knowledge fragmentation, which may lead to insufficient depth of language understanding and inefficient learning. This study constructs an intelligent teaching architecture that integrates knowledge mapping and intelligent semantic analysis. The architecture establishes a knowledge map of English intercultural communication through systematic construction of English intercultural communication neighborhood ontology, knowledge extraction and integration. On this basis, an intelligent semantic analysis model is designed, which combines a named entity recognition method based on the self-attention mechanism and an intention recognition method based on ALBERT-TextCNN to deeply understand learners' linguistic input. The results of the empirical study show that there is a significant improvement in the language comprehension of students in the experimental group in English intercultural communication scenarios compared to the control group in the traditional teaching model, and the number of students in the experimental class who regularly communicate with native English speakers outside the classroom has increased by 40% compared to the control group. This study confirms the positive effects of intelligent semantic analysis technology on deepening language comprehension and optimizing the efficiency of intercultural acquisition, and provides a more intelligent path for the reform of English intercultural communication teaching.