In the upcoming sixth-generation (6G) communication system, to guarantee the realtime performance and high reliability of intelligent service, artificial intelligence (AI) is indispensable. Accurate prediction of cell traffic is essential in networking, such as energy-saving, load-balancing, handover, and ensuring the quality of service (QoS) for 6G everyone-centric customized services. In the scenario of cell traffic, inter-cell correlations, such as distances and handover operations between cells can directly affect the intra-cell traffic load concurrently. In this work, we proposed a multi-hierarchical spatial-temporal graph convolution network (MH-STGCN) to predict cell traffic indicators. The hierarchical graphs explicitly import different inter-cell correlations to improve the performance by associating these correlations with intra-cell indicators and multiple graphs allow to consider different correlations simultaneously. Therefore, MH-STGCN is capable to import correlations from multiple sources simultaneously. Experiments show that MH-STGCN is more reliable and improves MAE by 8.77% and RMSE by 26.14% on average from MTGNN. MTGNN outperforms the other baseline methods conducted on our dataset.