The invention discloses a quantum-behaved particle swarm optimization (QPSO) recurrent predictor neural network (RPNN) method for financial time series prediction, which relates to analysis and prediction on a time sequence. Chaos and phase space reconstruction theories are firstly applied, through a saturation correlation dimension (G-P) method, a chaotic financial time series attractor dimension is calculated, and the structure of the RPNN is determined; then, the RPNN is trained by the QPSO algorithm; and finally, the dynamic optimal weight and the threshold of the network are determined, and thus, the simulation prediction value of the RPNN and the actual value can reach the minimum error precision. The problem that optimization of the RPNN based on a gradient algorithm is easy to fall into local minimum can be solved, and the built QPSO-RPNN optimization prediction method can be widely applied in financial investment and social economy, and has the advantages that the convergence rate is quick, the searching is global, the programming is simple and efficient, and the prediction precision is high.