Graph neural networks (GNNs) have been widely applied in graph-based learning tasks. However, the feature collection stage is increasingly becoming the bottleneck of the existing GNN systems when training large-scale graphs. To solve this problem, this paper proposes FCGraph, a novel efficient GNN training strategy by combining a feature cache policy of multi-hop (k-hop) neighbors and a hierarchical GPU-centric data access method. First, a k-hop neighbors based cache policy is proposed by exploring vertex data access characteristics in k-layer GNN models, which focuses on reducing CPU-GPU data transfer overhead. Second, based on the cache policy, node features are partitioned according to node access frequency, and a novel GPU-centric data access method is presented for feature collection. Further, FCGraph is scaled to multi-GPU systems equipped with NVLink, in which the memory access hierarchies are explored. The evaluation of some representative datasets shows that FCGraph can improve end-to-end training performance over the advanced GNN training systems of DGL and PyTorch-Direct by 3.72 \(\times \) and 1.46 \(\times \) on average, respectively.