In the era of globalization and digitalization, the demand for efficient and accurate translation continues to grow, especially in key areas such as business, education and technology. This demand has led to the pursuit of more advanced translation technologies, especially in scenarios dealing with big data and complex contexts. The significance of the research is that by integrating data analysis and machine learning techniques, significant improvements in translation quality and proofreading efficiency can be achieved, thereby shortening translation cycles, reducing costs, and providing more customized multilingual, multi-domain translation services. Specifically, the intelligent translation proofreading system is able to use big data analysis to more accurately understand and translate subtle differences between different languages, as well as specialized terms in specific fields. In addition, these systems can continuously learn and adapt to new language usages and terminology, effectively improving translation accuracy and naturalness. Through the analysis of the experimental data of the article, it can be seen that the correct rate of translation proofreading of the system ranges from 90.15% to 97.51%, the fastest translation speed is 0.4 s to 0.9 s, and the average satisfaction of the four age groups is 83.85%. Through these experimental data, we can judge that the system not only improves the overall quality of translation, but also provides users with more convenient and efficient translation services, which meets the needs of the current digital globalization era.