The invention discloses a kernel extreme learning machine-based virtual extension method for leaked gas monitoring concentration data, and relates to the technical field of dangerous chemicals. The extension method comprises the steps of firstly selecting position point coordinates Xs and Ys and concentration data of a monitored space region S1 as a training sample set, wherein coordinate values are input values of a network, and the concentration data serves as an output value of the network, so that the network is constructed and trained; and secondly determining coordinates (XPn, YPn) according to space positions S2-S1 of virtual monitoring points needed to be extrapolated or interpolated, wherein n is a predicted point number, the predicted point number forms input values in a predicted sample set together with the coordinates in the training sample set and is input to the trained network, the output value of the network is a to-be-predicted target value, namely, gas concentration data of all monitoring points of a virtually extended space S2, and data on an initial monitoring surface S1 is kept unchanged. According to the method, the source characteristic inverse computation precision is effectively improved without adding the monitoring points; and moreover, the workload is reduced and the working efficiency is improved.