Information technology has become an important carrier for the implementation of flipped classrooms, giving full play to the role of modern education technology and transforming the traditional classroom teaching form into an important form in today’s education reform. This article mainly studies the simulation of the flipped classroom model of listening and speaking teaching for English majors based on artificial intelligence. A total of 31 English majors were selected for the experiment, including 7 boys and 24 girls. After obtaining the consent of school leaders and teachers, 16 weeks of experimental teaching were carried out in the class. The experimental subjects had not been exposed to the flipped classroom teaching mode before the start of the experiment, and they were willing to participate in this experiment. After the phased flipped classroom teaching mode was carried out in the experimental class, the teaching objects in the experimental class were selected in the form of interview questionnaires to understand students’ attitudes and acceptance of the flipped classroom teaching mode in English listening and speaking classes. Adjust and improve the poststage flipped classroom teaching model to provide reference. During the experiment, the control class adopted the multimedia courseware-assisted oral teaching method and, in the experimental class, flipped the classroom oral teaching mode. Teachers integrate resources when preparing lessons to design flip-flop classroom oral English teaching, and teachers guide students to use the internet to search for resources and preview independently. The average value of the English self-management learning ability of the students of this major was 27.48 points before the experiment started. After passing the experiment, the score increased to 38.90 points, and the average value increased significantly (t = −20.189, P < 0.01 ). A comparatively complete comparison of English listening and speaking teaching results was carried out. Before the implementation of the flipped classroom, the students’ average professional English score was 76.23 points, and the average score after the launch was 84.39 points. The overall average score increased by 8.16 points. With regard to students’ English learning scores, there has been some improvement. The results show that, through the implementation of flipped classrooms, students have exercised their ability to self-manage their learning and strengthened their ability to make learning plans, implement them, and reflect on the effectiveness of learning.

Multiobjective flexible workshop scheduling is an important subject to improve resource utilization and production efficiency and enhance the competitiveness of enterprises. As the situation of resource constraints becomes more and more severe, the problem of companies rationally allocating limited resources in production is becoming more and more serious. Today, the manufacturing industry widely adopts advanced manufacturing modes such as computer-integrated manufacturing and intelligent manufacturing, but in these semi-intelligent manufacturing modes with a high degree of uncertainty and a high degree of personnel dependence, it is difficult to adapt to the work of large-scale production. Therefore, suitable clustering algorithms are urgently needed to help solve these problems, and this paper selects a clustering algorithm based on the genetic simulation annealing algorithm. This article is aimed at studying the problem of efficiency improvement in the production process of large-scale manufacturing and at finding a stronger and more effective production mode for the manufacturing industry. Firstly, this paper introduces the basic principles of simulated annealing genetic algorithm and regularized clustering algorithm. These algorithms have excellent performance in searching for global optimal solutions. They can be constantly tested and computed to keep the calculation results close to the global optimal solution. In this paper, the K -means clustering algorithm is used to select the shortest completion time to represent the clustering target. According to the minimum distance principle, the machine, workpiece, and other objects are input into the clustering of the algorithm, and the K -means algorithm will send out the sorting plan. Therefore, a multiobjective flexible job shop scheduling model based on genetic simulated annealing algorithm and clustering algorithm is established. Then, by using hypothetical production data to simulate the operation of the workshop, the scheduling model was applied to conduct a deduction and empirical comparative study. The experimental results showed that the model shortened the completion time of the workpiece by 4.4% and increased the average load rate of the machine by 10%.

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