The　power　system　contains　a　variety　of　uncertainties　of　different　types　of　sources　and　loads,　as　well　as　random　contingencies.　Under　these　uncertainties　and　rapidly　changing　operating　conditions,　traditional　rule-based　methods　cannot　dynamically　handle　short-term　voltage　instability.　To　alleviate　this　situation,　this　paper　proposes　a　novel　power　system　emergency　control　scheme　using　a　data-driven　method,　which　combines　edge-conditioned　graph　convolutional　networks　and　deep　reinforcement　learning.　The　edge-conditioned　graph　convolutional　network　is　utilized　to　extract　the　characteristics　from　not　only　power　system　nodes　but　also　transmission　lines.　Deep　reinforcement　learning　is　introduced　to　perform　load　shedding　actions,　so　as　to　guarantee　the　safety　and　stability　of　the　electric　power　system.　The　IEEE　39-bus　network　is　utilized　for　simulations　to　validate　the　effectiveness　of　the　proposed　data-driven　method.　The　outcomes　demonstrate　the　proposed　method　can　generate　a　superior　strategy　in　a　number　of　the　short-term　voltage　instability(STVI)　circumstances.　©　Published　under　licence　by　IOP　Publishing　Ltd.