Abstract: From the perspective of energy configuration, each link in the value chain (production-distribution-storage-consumption) of the energy industry should be transformed into the grade closest possible to the load (electricity-oil-gas-storage) according to the load demand. The aim is to achieve efficient flow in the energy network and to improve the production, transmission, and utilization of energy in time and space. The present energy data are of strong spatial-temporal distribution characteristic with a huge quantity and wide categories, and thus functions such as real-time sensing, rule tapping, source tracing, and forecasting of the value chain link-related elements are impossible to be realized. This paper fully leverages the strengths of network technologies (big data, basic map data, location-based ecology, and visual engine), analyzes multiple spatial-temporal scales of the energy network, and puts forward a spatial-temporal intelligent application framework in the energy network. This paper also explores the directions of spatial-temporal intelligent application in the energy network by combining multi-source, multi-type, and multi-time scale energy data, helps to construct an efficient, safe, and interconnected energy network.