The permeability of distributed generation in distribution network increases gradually. In order to ensure the safe and stable operation of the distribution network, it is necessary to accurately perceive the operation state of distribution network. And Cubature Kalman filter (CKF) has limited filtering accuracy for the strongly nonlinear non-Gaussian system and the importance density function of standard particle filter (PF) is not inaccurate, an Cubature particle filter (CPF) algorithm is proposed to estimate the dynamic state of distribution network. The importance density function of PF is designed through using the cubature Kalman filter (CKF) algorithm. It not only overcomes the restriction of Gaussian distribution noise required in CKF algorithm, but also retains the strong anti-interference ability of PF algorithm. The simulation results show that CPF algorithm is more accurate and flexible than CKF algorithm under Gaussian noise and non-Gaussian noise.