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The stability and security of the power grid is deteriorated due to the recent large-scale power grid interconnection and the power marketing. The stability character of the power grid becomes complex more and more. The new theory and technique need to be applied to diagnose the real-time state of the power grid in order to ensure the safe and economical operation of the power grid. The power flow fingerprint character during the normal and fault state is analyzed and the power grid health diagnosis repository with the Self-learning ability is constituted. The diagnosis method adaptable for the wide area power grid health state diagnosis is put forward in the paper. The power flow character is extracted using AFIS technology, and the intelligent matching arithmetic is used to diagnose the power grid health state, so the power grid health state factor and the uncertain probability distributing between the failure and symptom is present. The early warning and decision support architecture is constructed based on the Power Flow Fingerprint Identification and MAS Technology. The health diagnosis results can be integrated with the other intelligent diagnosis results and can alarm the dispatcher using the technology of visualization. The application of power flow fingerprint identification technique to diagnose the health state of the power grid can eliminate the potential fault in the power grid and prevent the happening of the paroxysmal accident.