In view of the traditional particle swarm optimization (pso) algorithm convergence speed, it's easy to fall into local optimum and cause disadvantages such as low convergence accuracy and it is not easy to converge, an improved particle swarm algorithm of adaptive inertia weight is proposed. Through the analysis of particle flying speed and position change, combined with the adaptive value of particles used to dynamically adjust the weight, enables the algorithm to the to achieve a good balance between global search and local search. Choosing typical test functions, the improved particle swarm optimization (PSO-A), with compression factor of particle swarm optimization (PSO-X)and inertia weight linear decreasing of the particle swarm optimization (PSO-W) performance is analyzed. Finally, MATLAB software is used for simulation. The results show that the improved particle swarm optimization algorithm has apparent improved its convergence speed and accuracy.