Since fractional order chaotic time series prediction has low precision and slow speed, a prediction model of new orthogonal basis neural network based on Quantum Particle Swarm Optimization (QPSO) algorithm was proposed. Firstly, on the basis of Laguerre orthogonal basis function, a new orthogonal basis function was put forward combined with the neural network topology to form a new orthogonal basis neural network. Secondly, QPSO algorithm was used for parameter optimization of the new orthogonal basis neural network, thus the parameter optimization problem was transformed into a function optimization problem on multidimensional space. Finally, the prediction model was established based on the optimized parameters. Fractional order Birkhoff-shaw and Jerk chaotic systems were taken as models respectively, then chaotic time series produced according to Adams-Bashforth-Moulton estimation-correction algorithm were used as the simulation objects. In the comparison experiments on single-step prediction with Back Propagation (BP) neural network, Radical Basis Function (RBF) neural network and general new orthogonal basis neural network, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the new orthogonal basis neural network based on QPSO algorithm were significantly reduced, and Coefficients of Decision (CD) of it was closer to 1; meanwhile, Mean Modeling Time (MMT) of it was greatly shortened. The theoretical analysis and simulation results show that the new orthogonal basis neural network based on QPSO algorithm can improve the precision and speed of fractional order chaotic time series prediction, so the prediction model can be easily expanded and applied.