With the continuous development and progress of the times, college English courses should adapt to the requirements of the development of the information age in terms of teaching concepts, teaching contents, and teaching methods, so as to adapt to the changes of learners' personal needs and social needs. Because the traditional teaching mode ignores students' experience and perception, the development of students' language literacy is limited, which restricts the training efficiency of foreign language talents. The introduction of smart classroom can change this situation. The system can develop one-to-one learning methods based on students' mastery of foreign language knowledge and students' own strengths, so as to help students improve their English learning efficiency. This paper mainly analyzes the overall design of ICAI system on Massively Open Online Courses platform: the system design takes SSH structure as the framework and Back Propagation, and focuses on the basic principle, specific steps of training and heuristic rules of evaluation and application of BP algorithm. Smart classroom can truly realize personalized learning and teach students according to their aptitude, so that learners can get wisdom development. The Particle Swarm algorithm-Back Propagation prediction model is designed for student behavior data mining. The representative data are selected as the input of the neural network, and the course grade is selected as the output of the neural network. The grade prediction error is 12%, which provides a new idea for improving the teaching quality.
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