Business English Translation Model Based on BP Neural Network Optimized by Genetic Algorithm

Area of Science:

• Computational Linguistics
• Artificial Intelligence
• Business Communication

Background:

• Business English translation faces challenges due to linguistic and grammatical variations across cultures.
• Current translation methods, including manual interpretation and electronic equipment, have limitations in accuracy and efficiency.
• Optimizing business English translation technology is crucial for effective global communication.

Purpose of the Study:

• To introduce the specific purpose of business English translation.
• To identify the discrepancies between business English translation and general English translation.
• To enhance the accuracy and efficiency of business English translation models.

Main Methods:

• Utilizing a genetic algorithm (GA) to optimize the structure of a BP neural network.
• Combining GA with BP neural networks to improve translation search capabilities.
• Comparing the performance of a traditional BP algorithm with a GA-optimized BP algorithm for business English translation.

Main Results:

• The GA-optimized BP neural network significantly improves the speed of business English text translation.
• This optimized model effectively reduces the impact of semantic errors on translation accuracy.
• The study demonstrates a notable improvement in the overall efficiency of business English translation.

Conclusions:

• Genetic algorithm optimization of BP neural networks is a viable method for enhancing business English translation.
• The proposed model offers a more accurate and efficient solution for business communication in multicultural environments.
• Further research can explore advanced algorithms to address the complexities of business English translation.