There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students’ class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students’ learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students’ inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application.

In recent years, because of the popularity of the internet and mobile devices, the dissemination of new media in social networks has attracted extensive attention from scholars and the industry. Scale prediction or propagation speed prediction is to use the initial data to predict the propagation scale of the network. In the complex and changeable social network, how to accurately predict the cascading scale of new media information is the biggest problem at present. In the process of new media information transmission, because of the role of new media information transmission in guiding public opinion, the current hierarchical model of new media information transmission lacks the overall and local models. To solve this problem, a global structure modeling method is proposed. In addition, because of the uncertainty of new media information dissemination, a method of bidirectional recurrent neural network prediction and algorithm complexity is used, and a new method based on large-scale graph neural network is constructed. A prediction method of new media information dissemination speed and scale based on large-scale graph neural network. Through comparative experiments with previous research models, it is found that the NWIDF model constructed in this paper has a good prediction effect.

With the current exchange and communication between different countries becoming more and more frequent, the language conversion of different countries has become a difficult problem. The analysis of a series of problems in cross-language discourse conversion, the study of the discourse conversion path, and innovation motivation based on the deep learning theory of cross-language transfer, it has theoretical and practical significance. This paper aims at the technical difficulties in speech conversion methods to effectively utilize the local mode information of signal time spectrum and the long-term correlation of speech signal. A discourse conversion method based on convolutional recurrent neural network model is proposed. In the model, the extended convolutional neural network is used to model the long-term correlation of speech signals. In the part of speech fundamental frequency estimation, the prosodic information generated by the decomposition of the fundamental frequency by continuous wavelet transform is used as the training target of the fundamental frequency estimation model. The experimental results show that the speech transformation method based on the convolutional cyclic network model proposed in this paper has better quality and intelligibility than the speech transformed by the contrast method.