Abstract: With the deepening of the research on flipped classroom teaching theory, the flipped classroom teaching model has gradually been applied to classroom teaching at all levels and types of schools, and some beneficial results and experiences have been obtained. Due to the relatively low self-learning ability and motivation level of students, in the implementation of flipped classrooms, the quality of preclass self-study links is difficult to guarantee, resulting in unsatisfactory results of flipped classroom teaching in secondary vocational schools. This article aims to solve the current dilemma faced by the optimization of the flipped classroom teaching mode of programming courses by studying the course platform based on the flipped classroom teaching model. The source-destination node distribution is constructed with a model based on node affinity to restore the actual network node distribution architecture. The change in the distribution of source-destination nodes has led to different degrees of aggregation in the overall data flow of the network. After that, the capacity and delay performance of the primary network and the secondary network will change as the degree of data flow aggregation changes. By laying base stations in the main network, we reanalyzed the network. Through the comprehensive analysis of students’ learning status through the scores of students in class and the test situation after class, we modify the specific teaching plan of flipped classroom. Experiments have proved that the in-class flipping model we proposed effectively avoids the inherent shortcomings of students who are not strong in autonomous learning before class, solves the problem that secondary vocational students cannot do well in autonomous learning before class, and improves students to a certain extent. The results show that the flipped classroom teaching model in class can provide more powerful value for vocational teaching to achieve this goal.
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