Feature Extraction of English Semantic Translation Relying on Graph Regular Knowledge Recognition Algorithm

Under the background of big data, people are not only pursuing the quantity but also the accuracy of knowledge in acquiring knowledge, especially for English. Because of the ambiguity, variety, and irregularity of English translation, people's reading has brought a lot of trouble. This paper aims to study the feature extraction of English semantic translation and suggests a recognition algorithm that relies on graph common knowledge. Through the analysis of graph regularization and the construction of the model, the recognition algorithm is improved, and the feature extraction methods are compared and analyzed. At the same time, experiments are intended to investigate the improvement of the English semantic translation of the improved recognition algorithm after feature extraction. The experimental results in this paper show that the improved English semantic translation has increased by 10%-15% in terms of translation accuracy. This degree of improvement has great application significance in actual English semantic translation.