The Flow excitation fault is a common fault of gas turbine due to the working medium. Aiming at the flow excitation fault of the gas turbine, deep belief network (DBN) model is established to realize fault diagnosis based on the peak hold down sampling (PHDS) algorithm and particle swarm optimization (PSO) algorithm. The vibration data which is compressed by the PHDS algorithm is used as the input of the DBN to reduce the training time of the model. The PSO algorithm is adopted to optimize the structure parameters of the DBN to find the model with the best diagnostic effect. The results of example show that the optimized model not only reduces the training time of the model, realizes the intelligent optimization of network structure parameters, but also diagnoses the flow excitation faults of gas turbine effectively and the accuracy of the test was about 998%.