The techniques for oceanographic observation have made great progress in both space-time coverage and quality, which make the observation data present some characteristics of big data. We explore the essence of global ocean dynamic via constructing a complex network with regard to sea surface temperature. The global ocean is divided into discrete regions to represent the nodes of the network. To understand the ocean dynamic behavior, we introduce the Gaussian mixture models to describe the nodes as limit-cycle oscillators. The interacting dynamical oscillators form the complex network that simulates the ocean as a stochastic system. Gaussian probability matching is suggested to measure the behavior similarity of regions. Complex network statistical characteristics of the network are analyzed in terms of degree distribution, clustering coefficient and betweenness. Experimental results show a pronounced sensitivity of network characteristics to the climatic anomaly in the oceanic circulation. Particularly, the betweenness reveals the main pathways to transfer thermal energy of El Ni?o–Southern oscillation. Our works provide new insights into the physical processes of ocean dynamic, as well as climate changes and ocean anomalies.