Abstract: In combination prediction of IP network traffic, the single model’s mathematical characteristic, prediction accuracy and weight coefficient have significant impact on combination prediction results. As the grey model can depict linearity characteristics of network traffic and the BP neural network model can depict the non-stationary and non-linear characteristics, a Fuzzy Self-Adaptive Variable-Weight Combination Prediction Model (FSVCPM) was composed of them. To improve the prediction accuracy of single model as far as possible, a improved residual grey prediction model was established via indexation processing of residual sequence. By training experiments, neuron number of input layer and hidden layer was identified and corresponding BP neural network was given. By introducing fuzzy decision mechanism and self-adaptive mechanism to calculate fuzzy weight and basic weight, FSVCPM was built and a determination method of variable-weight coefficient was addressed which can make single models to fit effectively. Experimental results validated the correctness and accuracy of the FSVCPM and proved the prediction precision was higher than that of the single model and the Constant-Weight Combination Prediction Model (CCPM).