AutoEncoder （AE） is an efficient learning model for graph data representation， but most of the Graph AutoEncoders （GAEs） are shallow models， their efficiencies decrease with the increase of hidden layers. Aiming at the above problems， a new GAE model OSA-GAE and a new Variational Graph AutoEncoder OSA-VGAE were proposed based on One-Shot Aggregation （OSA） and Exponential Linear Unit （ELU）. Firstly， the encoder was constructed by a multi-layer Graph Convolutional Network （GCN）， and OSA and ELU function were introduced. Then， the topology of the graph was recovered by the inner product decoder in the decoding stage. In addition， a regularization term was introduced to the loss function in order to prevent parameter overfitting during the model training process. Experimental results show that OSA and ELU function can improve the performance and gradient information transmission of the deep GAEs. In the link prediction task of benchmark citation dataset PubMed， when using 6-layer GCN， deep OSA-VGAE improves Area Under ROC Curve （AUC） and Average Precision （AP） by 8.67 and 6.85 percentage points respectively compared to the original VGAE， and deep OSA-GAE improves AP and AUC by 6.82 and 4.39 percentage points respectively compared to the original GAE.