Particle Swarm Optimizer ( PSO) has the problems of long running time and easy to fall in to local optimality when dealing with the optimization of large data. In this paper, based on the traditional particle swarm optimization algorithm, the feature selection method based on factor space is used to optimize the combination of feature data and reduce the running time of the algorithm. The fuzzy multi-population particle swarm optimization algorithm based on factor space is proposed by combining the idea of multi-population particle swarm optimization model with the idea of fuzzy mathematics, which enhances the global optimization ability of the algorithm and speeds up the convergence rate, thus achieving the overall optimization of the algorithm. The traditional particle swarm optimization algorithm is easy to converge to the local optimal defect. The results show that compared with the traditional particle swarm optimization algorithm, dynamic multi-population particle swarm optimization algorithm and multi-population particle swarm optimization algorithm based on density peak, the fuzzy multi-population particle swarm optimization algorithm based on factor space proposed in this pa per has a better ability to find the global optimal solution and convergence speed.