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When the PV module fails, its internal parameters should reflect the fault characteristics of the PV module. Based on the comparison of the internal parameters of the PV modules under different fault conditions, the corresponding fault characteristics are extracted. First, the fault models of PV modules are established by MATLAB. Quantum particle swarm algorithm is used to identify the PV modules under different fault types and obtain the corresponding data. The BP neural network fault diagnosis model is established by using the data and the fault type is diagnosed. The simulation results show that the parameter identification method can accurately identify the PV modules Parameter identification, the diagnostic results also show that the selected fault feature is reasonable, through this model can accurately diagnose the PV module failure.