Abstract
The primary focus of artificial intelligence advancement is in machine translation; nonetheless, a prevalent issue persists in the form of imprecise translation. The current challenge faced by artificial intelligence is to effectively executing machine translation from extensive datasets. This research presents a BP neural method that aims to repeatedly analyse translation data and achieve optimisation in machine translation. The findings indicate that the use of BP neural network may enhance the dependability and precision of machine translation, with an accuracy rate over 84%. This performance surpasses that of the online translation approach. Hence, it can be inferred that the use of BP neural algorithms has the potential to fulfil the requirements of machine translation and enhance the precision of online translation conducted by humans.
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