Blended learning has taken center stage in higher education because it involves overseeing of “Online learning” and the ongoing growth of teaching reform in colleges and universities. Internet promotes the reconfiguration and integration of educational resources. The Internet has greatly magnified the role and value of high-quality educational resources. Blended teaching combines the advantages of traditional teaching and network teaching to complement each other; it not only gives full play to the flexibility and autonomy of network teaching, but also retains the connection of teacher-student emotional communication in traditional teaching. Translation teaching is an important part of college English teaching. This article mainly introduces the research of college English translation (CET) blended teaching strategies under the Internet background. This paper proposes a research plan for CET blended teaching strategies under the Internet background, including literature research method, questionnaire survey method, analytic hierarchy process, expert interview method, teaching evaluation sentiment classification algorithm based on feature-weighted Stacking algorithm, and analytic hierarchy process of the teaching effect evaluation algorithm. The experimental results of this article show that the average value of the questionnaire Cronbach’s α coefficient is 0.915, demonstrating that the study's information is both extremely plausible and reasonably real.

With the rapid development of Internet technology and the development of economic globalization, international exchanges in various fields have become increasingly active, and the need for communication between languages has become increasingly clear. As an effective tool, automatic translation can perform equivalent translation between different languages while preserving the original semantics. This is very important in practice. This paper focuses on the Chinese-English machine translation model based on deep neural networks. In this paper, we use the end-to-end encoder and decoder framework to create a neural machine translation model, the machine automatically learns its function, and the data is converted into word vectors in a distributed method and can be directly through the neural network perform the mapping between the source language and the target language. Research experiments show that, by adding part of the voice information to verify the effectiveness of the model performance improvement, the performance of the translation model can be improved. With the superimposition of the number of network layers from two to four, the improvement ratios of each model are 5.90%, 6.1%, 6.0%, and 7.0%, respectively. Among them, the model with an independent recurrent neural network as the network structure has the largest improvement rate and a higher improvement rate, so the system has high availability.

With education and teaching reform, Internet teaching is increasing. The introduction analysis of short text messages generated by e-learning can optimise class, improving innovation capacity and cooperation. To study the way of instructional design under artificial intelligence, the current status of the development of instructional has to be mentioned. In order to study the impact of online teaching on the improvement of computer thinking and skills in higher vocational education, this article is based on online English teaching embedded in the Internet and on virtual simulation technology, to enable primary school students to cultivate computer education thinking from an early age. The personalities can be fully represented and developed.

With the development of linguistics and the improvement of computer performance, the effect of machine translation is getting better and better, and it is widely used. The automatic expression translation method based on the Chinese-English machine takes short sentences as the basic translation unit and makes full use of the order of short sentences. Compared with word-based statistical machine translation methods, the effect is greatly improved. The performance of machine translation is constantly improving. This article aims to study the design of phrase-based automatic machine translation systems by introducing machine translation methods and Chinese-English phrase translation, explore the design and testing of machine automatic translation systems based on the combination of Chinese-English phrase translation, and explain the role of machine automatic translation in promoting the development of translation. In this article, through the combination of machine translation experiments and machine automatic translation system design methods, the design and testing of machine automatic translation systems based on Chinese-English phrase translation combinations are studied to cultivate people's understanding of language, knowledge, and intelligence and then help solve other problems. Language processing issues promote the development of corpus linguistics. The experimental results in this article show that when the Chinese-English phrase translation probability table is changed from 82% to 51%, the BLEU translation evaluation system for the combination of Chinese-English phrases is improved. Automatic machine translation saves time and energy of translation work, which shows that machine translation shows its advantages due to its short development cycle and easy processing of large-scale corpora.