Since the selection of the main parameters of the support vector machine can affect the classification performance and effect to a large extent, and the current parameter optimization lacks theoretical guidance, a particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. This method improves the shortcomings of the standard particle swarm optimization algorithm with slow convergence rate and easy to fall into local optimum by introducing nonlinear decreasing inertia weight and asynchronous linear variation learning factor strategy. The experimental results show that compared with the standard particle swarm optimization algorithm, the proposed method has good robustness, fast convergence and global search ability in parameter optimization, and has higher classification accuracy and efficiency.