In order to solve the problem that the particle swarm algorithm does not have high search ability in the late iteration and the particles tend to fall into the local optimum when it is introduced into a nonlinear financial risk model (objective), a financial investment risk control model based on an improved particle swarm algorithm is proposed. Based on the optimization of inertia weights and the variation of individual position of each particle, an improved particle swarm algorithm is proposed. The particle swarm algorithm is used to select the optimal control parameters to minimize the total risk value of the financial system. The simulation results show that: in the traditional algorithm, the beginning stage drops faster, and the local optimal phenomenon appears soon, but the improved algorithm makes the particles jump out of the local optimal trap soon by improving the inertia weights and other values, so as to reach the optimal faster, and the convergence of the improved algorithm is advanced more than 5 generations.