Abstract: In the traditional particle swarm optimization (PSO) algorithm, all particles follow some initial parameters to explore themselves. This scheme is easy to lead to premature maturity, and easy to be trapped in the local optimum. To solve the above problems, a particle swarm optimization algorithm based on deep deterministic policy gradient (DDPGPSO) is proposed. The action function and action value function are realized by constructing neural network. The parameters required by the algorithm can be generated dynamically by using the neural network, which reduces the difficulty of manual configuration of the algorithm. The experimental results show that DDPGPSO has a great improvement in convergence speed and optimization accuracy compared with nine similar algorithms.