Compared with the traditional graph data analysis method, graph embedding algorithm provides a new graph data analysis strategy. It aims to encoder graph nodes into vectors to perform graph data analysis or mining tasks more effectively by using neural network related technologies. And some classic tasks have been improved significantly by graph embedding methods, such as node classification, link prediction, and traffic flow prediction. Although plenty of works have been proposed by former researchers in graph embedding, the nodes embedding problem over temporal graph has been seldom studied. This study proposed an adaptive temporal graph embedding, ATGED, attempting to encoder temporal graph nodes into vectors by combining previous research works and the information propagation characteristics together. First, an adaptive cluster method is proposed by solving the situation that nodes active frequency is different in different types of graph. Then, a new node walk strategy is designed in order to store the time sequence between nodes, and also the walking list will be stored in bidirectional multi-tree in walking process to get complete walking lists fast. Last, based on the basic walking characteristics and graph topology, an important node sampling strategy is proposed to train the satisfied neural network as soon as possible. Sufficient experiments demonstrate that the proposed method surpasses existing embedding methods in terms of node clustering, reachability prediction, and node classification in temporal graphs.