Construction of an English Translation Intelligent Recognition System Based on BP Neural Algorithm

Intelligent English translation automatically inputs, processes, transmits and outputs English information by means of speech recognition and natural-language understanding. Most of the methods currently used are based on machine learning algorithms, but there are many defects in training, resulting in low translation efficiency and accuracy. This article introduced the BP (back propagation) neural network into the process of English translation recognition, constructed an intelligent model that can achieve online fast classification, and verified through experiments that the model had good predictive ability and generalization, providing new ideas and technical approaches for solving English vocabulary retrieval problems. Comparing the translation system based on BP neural network with the system based on Support Vector Machine (SVM), the recognition results showed that the average recognition accuracy of the system based on BP neural network for translated text was about 3.42% higher than that of the system using SVM. In practical applications, it can effectively improve English recognition speed and enhance user experience. The BP neural algorithm overcomes the shortcomings of traditional artificial neural networks, such as high computational complexity and being prone to falling into local extreme points, and provides an efficient and practical solution for the field of English translation.