In order to improve the accuracy rate of aeroengine gaspath fault diagnosis based on BP neural network, this research uses the genetic algorithm to optimize the initial weights and thresholds of BP neural network in their solution space, retrains the results by gradient descent algorithm and uses the optimized network to testify the fault samples. The result shows that GABP network has a higher precision and converges faster, and its convergence curve is smoother than that of the common BP network. This work can put forward new ideas and methods for aeroengine fault diagnosis and has a certain research value.