The study identifies the relevant semantic dependencies after establishing a corpus system database, and then searches for the degree of differentiation of the utterances through the English sentence similarity algorithm, which adopts the vector space modeling criterion and uses the computed similarities as vector elements. Meanwhile, two simple and efficient labeling transformation algorithms, namely, label transformation algorithm and graph-to-graph linear transformation algorithm, are proposed as a way to improve the performance of language learning in cross-cultural language communication. Based on the above, the study develops an AMR intelligent semantic analysis system using the stack-LSTM algorithm and analyzes its role in enhancing intercultural communication skills during English language acquisition. The accuracy of annotation can be verified by applying the system to the automatic syntax of English and Chinese languages, and then the utterance annotation is recognized on the corpus with different components, and the results show that the AMR intelligent semantic analysis system is highly accurate, with 92% recognition precision rate and good recognition effect. Finally, regression analysis is conducted according to the effect of using the intelligent semantic system on language acquisition by different groups of people, and it is found that the use of the intelligent semantic system has a significant effect on the improvement of students’ language communication ability.