Discrete Dynamic Modeling Analysis of College Students' Ideological and Political Education Based on Particle Swarm Optimization Algorithm

Abstract

Today, with the rapid development of the Internet, the new carrier and platform of college students' ideological and political education innovation need to be improved. In order to improve the timeliness of college students' ideological and political research, in view of this characteristic, particle algorithm is studied. Combined with the principle of particle algorithm, the core idea of particle algorithm is applied. College students think about their past behaviors and conduct self-evaluation and reflection. On the other hand, it is the competition and cooperation between multiple academic groups. Combined with particle swarm optimization algorithm, through model optimization, deal with the fitness value between the two attributes to optimize the work. Positive and negative excitation measures are introduced in the experimental research, and the particle swarm optimization evaluation function and behavior weighting factor are analyzed to hypothetically describe the working method. At the same time, it is pointed out that the educational working methods should be adjusted in time according to the changes of student groups and individual behaviors, so as to achieve good work results. Research shows that positive incentives are better than no incentives, and the introduction of negative incentives can only prevent college students from becoming negative role models because they cannot give them the best state of consciousness.