Contingency analysis is thekey computational issue in power system steady state security analysis and reliability calculations. This task requires a large amount of CPU time. In order to reduce effectively requirements of computationin outagesimulation ofbulk power system,a functioned link neural network (FLNN) classified model and algorithm employed to identify contingencies is presented. For the sake of gaining post-accident information of system states, a group of performance index (PI) is designed according to the performance characters relative to the changes of base case.Moreover, a neural network classifieris constructed. A varietyof the effects of PI and combinations of PI on the proposed classifieris discussed. That branch flow performance indices are better than the others is explanted. The resultsof classification by applied the FLNNclassifierto the IEEE-RTS24 show that it not only make network and algorithmsimpler, but also improvethe speed and accurate of contingency analysis.