A method for optimizing a support vector machine (SVM) classification model on the basis of a particle swarm optimization (PSO) algorithm, relating to the technical field of computer artificial intelligence. On one hand, an inertia weight is adjusted according to particle fitness, so that adaptive adjustment of the inertia weight is implemented, the diversity of the inertia weight is increased, and a global exploration capability and a local search capability of a PSO algorithm are better balanced. On the other hand, the time of particle mutation can be better controlled by using a threshold value calculated by means of the position of a successfully found particle as a mutation condition, the capability of the particle to jump out of the local best solution is improved after the mutation of the particle, and optimizing an best value of a parameter of an SVM is facilitated, and thus the classification accuracy of an SVM algorithm is improved. By optimizing the parameter of the SVM classification model, the present invention improves the classification accuracy of the SVM classification model, and promotes wider applications of the SVM classification model in the fields of model identification, system control, production scheduling, computer engineering, and electronic communications.
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