Limited by the irregularity of graph topology, permutation independence of graph nodes and variability of graph node scale, the majority of neural networks designed for graph classification adopts simple aggregation or sort operation to generate graph-level representation, leading to over-compression and translation of features. In order to address such problems, this paper proposes a novel graph convolutional network, called Globally Aligned Graph Convolutional Network（GAGCN），where graph-level representations are globally aligned by constructing a sub-structural approximate distribution. GAGCN can not only avoid over-compression and feature translation, but also utilize sub-structural distribution to further learn the internal structural similarity among graph data, thereby effectively improving the efficiency of information mining for the downstream classification network and the inference ability for graph classification, respectively. Experimental results show that GAGCN achieves superior results and improves 2%~6% average accuracy on a range of graph datasets，compared with several state-of-the-art graph classification algorithms. The ablation study and the hyper-parameter analysis further reflect the effectiveness and robustness of our approach.