Traditional network embedding approaches rely heavily on random walk in a node perspective to get the local sampling sequence of networks and then maximize the co-occurrence probability between adjacent nodes to represent nodes as low-dimensional vectors. The empirical analysis of this study on a real-world network shows that random walk in node and link perspectives can respectively produce network sampling results with different node frequency distributions, resulting in various partitions of the network. To this end, this study proposes an approach to Dual Perspective Based Coupled Network Embedding (DPBCNE). DBPCNE gets the network sampling sequences by random walk in a link perspective and then combines node sequences sampled in a node perspective for coupled training. Experiments show that compared with other network embedding approaches, this approach can well preserve network structures and improve the effectiveness of network embedding for the downstream classification and prediction tasks.