The research on improving English cross-cultural communicative competence by using big data analysis aims to explore how to improve individual cross-cultural communicative competence through data analysis technology under the background of globalization. In this study, natural language processing (NLP) and data mining technology are used to collect English cross-cultural communication data from several internationally renowned online communication platforms, social media and academic forums, and a multilingual and multi-domain English cross-cultural communication corpus is constructed. Through in-depth analysis of the corpus, this paper reveals high-frequency vocabulary, common expressions, communication strategies and emotional tendencies in cross-cultural communication, and then puts forward scientific guidance to improve English cross-cultural communication ability. It is found that in English cross-cultural communication, communication and understanding of cultural differences, direct and clear communication strategies and positive and friendly emotional attitudes are very important for effective communication. Through the mining of association rules, the strong correlation between key elements such as “cultural differences” and “understanding”, “communication” and “obstacles” is revealed, which provides valuable reference for teaching and practice. This study not only enriches the theoretical research of cross-cultural communication ability, but also provides a new perspective and method for English teaching and learning through big data analysis.