Focusing on the shortages of easily falling into local optimal, low convergence accuracy and slow convergence speed in Particle Swarm Optimization (PSO) algorithm, an improved Particle Swarm Optimization based on HierarChical autonomous learning (HCPSO) algorithm was proposed. Firstly, according to the particle fitness value and the number of iterations, the population was dynamically divided into three different classes. Then, according to characteristics of different classes of particles, local learning model, standard learning model and global learning model were respectively adopted to increase particle diversity and reflect the effect of individual difference cognition on performance of algorithm and improve the convergence speed and convergence precision of algorithm. Finally, HCPSO algorithm was compared with PSO algorithm, Self-adaptive Multi-Swarm PSO algorithm (PSO-SMS) and Dynamic Multi-Swarm PSO (DMS-PSO) algorithm on 6 typical test functions respectively. The simulation results show that the convergence speed and convergence accuracy of HCPSO algorithm are obviously higher than these of the given algorithms, and the execution time difference of the proposed algorithm and basic PSO algorithm is within 0.001 orders of magnitude. The performance of the proposed algorithm is improved without increasing complexity.