In order to better adapt to the relevant requirements of blended teaching in colleges and universities under the ecological environment and in the “Internet +” era, the “online + offline” blended teaching mode of college English in the Internet era is innovated to further highlight the subjectivity of students in the process of English learning status. First, this study deeply understands the current situation and problems of College English teaching from the perspective of the Internet by means of a questionnaire survey. On this basis, with the construction of the English teaching concept of “student-centered and teacher-led” as the core, this study puts forward and designs the “online and offline” mixed teaching mode of College English based on the support of mobile network technology. Through the teaching experiment, it is found that the total score of students in the mixed teaching mode is 6 points higher than that before, and the scores for English Cloze, writing, and reading are 2.75 points, 1.3 points, and 1.134 points, respectively. The feasibility and remarkable effect of the mixed teaching mode are verified.

The English translation is an important means of converting language as the basis of communication, is gradually becoming an indispensable part of people's daily life. With the rapid progress of science, cultural exchanges between countries in the world have become more and more common. And the advent of the Internet era has led to more frequent exchanges between different languages, and the demand for language translation has gradually increased. How to translate English quickly and accurately is the current challenge of machine translation tasks. In this paper, we investigate how to use the neural network model for the English translation in order to better assist the establishment of the machine translation system. We design an English translation method based on an encoder-decoder structure and an attention mechanism. First, we analyze the characteristics of the LSTM model. Second, we design an English translation framework using the seq2seq model. Third, we combine the attention mechanism to build a more robust translation model to improve the translation performance of the model. Finally, we validate the translation performance of our model on two public datasets and experimental results prove that the method proposed in this paper has good evaluation performance.