Aiming at the problem that the inertial component of particles could not guide the particle to the right direction when the fitness became poor, a multi-objective particle swarm algorithm learning factor improvement method based on the fitness change was proposed. The large learning factor improved the multi-objective particle swarm algorithm. In the simulation experiment, the improved algorithm PSO-AIC1C2 and the PSO-S, PSO-AIC1 and PSO-AIC2 with c 1 and c 2 obtained by splitting this algorithm were fixed with c 1 and c 2 changed separately, and then compared with other PSO improvements. The algorithms MOPSO, SMPSO, and dMOPSO are compared. Experiments showed that increasing c 1 could improve the performance of the algorithm, and increasing c 2 would cause the convergence of the algorithm to deteriorate. In most test functions, PSO-AIC1C2 had obvious advantages in convergence and distribution indicators. The improved method proposed had certain guiding significance for the study of learning factors of particle swarm optimization in the future.