Aiming at the problem that the accuracy of network traffic prediction is not high enough，a network traffic prediction method integrating multiple models is put forward in combination with currently popular Ensemble Learning.The weather factors are quantified as input，and 7 machine learning models are used to predict the network traffic respectively.Then，based on the analysis of the Pearson correlation coefficients of the prediction results of different models，5 models with weak correlation are selected as the basic model of stacking to predict network traffic and compare it with predictions that do not consider weather factors.The results show that the stacking method has better performance than the basic models.At the same time，the addition of weather factors makes the accuracy of the model’s prediction results improved. Compared with the neural network method，the Stacking method combines different prediction methods,the data can be processed from different angles with each basic model，and the prediction results are more accurate than the general method.It has certain practical value for the prediction of network traffic.