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Résumé : Aiming at the long-term prediction problem of wireless communication network traffic, a base station network traffic prediction method was proposed based on Wide & Deep learning. Firstly, S-H-ESD (seasonal hybrid extreme studentized deviate test) algorithm and data smoothing method were used to preprocess the network traffic data, and to reduce the impact of noise data on the prediction. Then, the network flow was input into the deep part (neural network) of the Wide & Deep model, the radio resource control (RRC) and physical resource block (PRB) were input into the wide part (linear model) of the Wide & Deep model, and the deep and wide parts were combined to predict the network traffic. The method established one prediction model for the network traffic of all base stations. The root mean squared logarithmic error (RMSLE) of prediction results was 0.985, which was significantly better than that of the traditional seasonal autoregressive integrated moving average model (RMSLE was 2.095) and that of the long short-term memory network model (RMSLE was 3.281). Experimental results showed that the Wide & Deep model could better solve the problem of long-term prediction of wireless network traffic via combining the memory ability of the linear model and the generalization ability of the depth model.