An Intelligent Recognition Model of English Translation Teaching Method Based on Improved GLR Algorithm

At present, machine translation is widely used in English translation, and the GLR algorithm has great practical value in the translation process. Usually, the number of recognition results calculated by the GLR algorithm has a lot of chance, and in the process of recognition, the accuracy of the algorithm is not very high, and for students, this method can also improve the teaching effect of English translation. However, the recognition speed of this algorithm is slow, and there are still some problems in practical application. This paper proposes an improved GPS algorithm to intelligently recognize it. This paper firstly proposes an optimized GLR algorithm based on intelligent recognition, and then and applies the neural network method to English translation teaching. Then, the phrase structure is constructed by the center point of the phrase, and then the improved GPS algorithm is used for translation, and the method is applied to English learning. It is proved by experiments that the neural network based on intelligent recognition can recognize English and Chinese words. Finally, the neural network model is applied to English translation teaching, and the intelligent recognition performance of the method is tested. The results show that the improved GPS algorithm has a certain contribution in improving students' learning efficiency.