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In the accelerating process of globalization today, communication and cooperation between different countries and regions have become increasingly frequent (Mehmood et al., 2021; Mai et al., 2024). However, language differences often become communication barriers, hindering the smooth progress of cross-cultural communication (Ban et al., 2021; Datta et al., 2020). In this context, the development of intelligent English oral translation technology has become particularly important, playing an indispensable role in cross-regional cultural exchange, international conferences, business negotiations, and tourism services (Datta et al., 2020). The development of English oral translation technology has profound significance in promoting international cooperation, enhancing cultural understanding, and promoting economic development. Although traditional rule-based machine translation methods have played a certain role in reducing language differences, with the continuous progress of artificial intelligence technology, the emergence of deep learning methods has brought new possibilities to oral translation technology. Recurrent neural networks (RNNs) have shown significant advantages in oral translation, especially in the case of a deep learning model that can capture long-term dependencies in sequence data (Wu et al., 2022). The aim of this study is to conceive and develop an intelligent English oral translation system based on the RNN model to achieve real-time seamless conversion of English oral language. Through in-depth exploration of the key stages in system development, we hope to emphasize the crucial role of RNNs in reshaping the landscape of oral translation technology and overcoming technical challenges in oral translation. With the continuous development of RNN-driven translation systems, we believe they will play an increasingly important role in promoting cross-cultural communication, supporting education, and promoting technological progress.