With the development of big data, distributed support vector machine (SVM) has become a hot research topic in this field. The process of finding the global optimal support vector in the Hadoop platform is long under the traditional hierarchical Cascade SVM algorithm. This paper presents an improved method by firstly combining the traditional grid method and the particle swarm optimization(PSO) algorithm to improve the PSO algorithm. And a new satellite parallel PSO algorithm is realized by combining the single machine PSO algorithm and the Hadoop platform (NPP-PSO). The experimental results show that compared with the single SVM algorithm, the distributed SVM algorithm cannot only ensure the accuracy but can also greatly boost the computation speed. With the wide use of NPP-PSO distributed SVM, the classification accuracy has improved significantly.