With the rapid development of mobile communication technology， wireless networks are facing multiple challenges， including resource allocation， traffic analysis， and 6G base station optimization. Effective prediction of wireless network traffic helps to allocate network resources reasonably and provides users with more stable and efficient services， ensuring network performance. To solve the problem of low prediction accuracy in the current wireless network traffic predictions due to insufficient mining of spatial and temporal features， this paper conducts research on intelligent traffic prediction algorithms based on deep learning methods， and proposes a prediction algorithm based on graph convolutional network-long short-term memory (GCN-LSTM） model. Experimental results show that the accuracy of this algorithm is 84.71% in actual network applications， which is superior to other deep learning-based traffic prediction methods， providing strong support for the rational allocation of 6G network resources and efficient service.