For the unbalanced datasets, the traditional fuzzy support vector machine (FSVM) algorithm classification effect is not obvious, and the introduced parameters are not optimized. Therefore, this paper proposes an improved fuzzy support vector machine(IFSVM)algorithm based on particle swarm optimization(PSO)algorithm, i.e. PSO-DEC-IFSVM algorithm. First, the algorithm is used to design fuzzy membership function considering the distance from training sample to its center, the tightness around the sample and the amount of information of the sample, and then IFSVM algorithm is combined with different error costs(DEC)algorithm for obtaining the DEC-IFSVM algorithm. Finally the PSO algorithm is used to optimize the introduced parameters in the DEC-IFSVM algorithm. Experiments show that the PSO-DEC-IFSVM algorithm has better positive and negative classification effect and stronger robustness than the existing FSVM algorithm and its improved algorithm for the six unbalanced data sets, such as Pima in UCI public data set.