Research on Business English Translation Model Based on Weighted Multi-Objective Deep Belief Network

Business English translation plays a crucial role in promoting international trade and technological exchange, but traditional translation methods suffer from issues such as low efficiency and insufficient accuracy. To address these challenges, this paper proposes a business English translation model based on Weighted Multi objective Deep Belief Network (WM-DBN), combined with artificial intelligence (AI) and fifth generation mobile communication (5G) technology, aiming to improve translation quality and shorten modeling time. This model constructs a deep network structure by stacking multiple Restricted Boltzmann Machines (RBMs) and optimizes it using a strategy that combines unsupervised layer by layer pretraining with supervised fine-tuning to better capture the complex dependencies between texts. This study not only effectively improves the quality and efficiency of business English translation, providing strong support for information flow and communication between multinational enterprises, but also provides new technical ideas for translation work in other fields.