English is now one of the most important languages for economic exchange in various countries around the world, and it is also the most widely used language for cultural and information exchange. Like other countries, China likewise attaches highest significance to English learning, and people’s demand for applied learning is also increasing rapidly these days. However, there are significant differences between Chinese pronunciation and English pronunciation, and China lacks an English environment while teaching English language. Furthermore, the traditional education is limited by the place and time of classes, due to which it cannot meet people’s needs for learning English. With the fast progress of computer knowledge, the emergence of deep learning technology can better identify English pronunciation and evaluate the quality of English pronunciation. Additionally, deep learning can provide learners with precise, objective, and rapid pronunciation information. It can also assist learners in determining the differences between their pronunciation and conventional pronunciation through frequent listening and comparison, as well as correcting their pronunciation faults and increasing language learning efficacy. This study looks into the difficulty of using deep learning to evaluate the quality of English speech recognition and pronunciation. To evaluate English pronunciation quality, this paper selects intonation, speed, and rhythm, as the distinguishing indicators. The comparison between the results of manual evaluation and our evaluation clearly shows that English speech recognition and pronunciation quality model using deep learning established in this paper has much higher reliability. Among the 240 samples tested, only 32 samples differ by one grade, and the rest are similar.

The role of emotions in second/foreign language education has been exponentially highlighted in the literature. However, the interplay of English as a foreign language (EFL) teachers’ hope, trust, and grit has witness a scant attention among L2 researchers. Against this shortcoming, the present mini-review article made an effort to offer a theoretical analysis of the theoretical and empirical underpinnings of these three constructs. In so doing, it presented the definitions, conceptualizations, dimensions, theories, related studies, and the way these variables can influence one another. Drawing on scientific findings in the literature, this study proposed some implications for EFL teachers, teacher trainers, principals, and scholars to enhance their knowledge of psycho-emotional factors and how establishing an environment based on hope and trust can generate success in L2 education. Finally, some recommendations for future research are made to drive this line of research forward.

With the rapid development of the Internet, although users can obtain information conveniently and quickly, they still cannot accurately obtain the target information they need in a short period of time in the face of massive information content. The use of embedded intelligent robots in the English news industry promotes the “Internet +” strategy, the goal of which is to connect everything. This article is aimed at studying the impact of embedded intelligent robots on the dissemination of English news. This article first analyzes embedded intelligent robots and news dissemination and then analyzes the collaborative filtering algorithm and text analysis technology of the recommendation system. Then, this article discusses the impact of embedded intelligent robot writing on English news dissemination and news media in the context of artificial intelligence. Direction of Development. Finally, in the experimental part, this paper implements a news recommendation system based on item recommendation algorithm and text analysis technology. Experiments have proved that the news recommendation algorithm of this system has a high hit rate and has great practical application value. This article sets each recommended page to 15 news, then randomly selects 5 users, and counts the number of news they visit, and the average number of news visited is 9.22. The results show that the embedded robot is helpful for English news dissemination, increasing the dissemination breadth by 20% and the dissemination accuracy by 30%.