Title: StepGCN: Step-oriented Graph Convolutional Networks in Representation Learning. Abstract: Graph Convolutional Networks (GCNs) are employed to address a number of tasks in our society with their representation learning approach. Nonetheless, despite their effectiveness and usefulness, the majority of GCN-oriented approaches have an over-smoothing concern. Over-smoothing is the problem of node representations converging into a certain value, making the nodes indistinguishable. To effectively address the over-smoothing problem, we introduce StepGCN, a GCN model that integrates step learning techniques with graph residual connection networks. With our StepGCN, we achieved significant performance improvements in multiple representation learning benchmark datasets, and demonstrate that step learning can be expanded to other graph networks. 