The improved Harris Hawks optimization algorithm (IHHO) is proposed to solve the problem that analog circuit fault diagnosis is difficult due to multiple fault types, unstable fault states and redundant fault data. IHHO optimized back propagation (BP) neural network to realize fault feature selection and diagnosis of analog circuits. Firstly, the nonlinear adaptive factor, Cauchy variation and stochastic difference perturbation are introduced into the Harris Hawks optimization algorithm to improve the convergence speed and accuracy. Secondly, IHHO is used to select the characteristics of the single fault and the combined fault simulation data of the analog circuit to complete the data preprocessing. Finally, IHHO-BP algorithm is used to train and test the preprocessed fault data to realize the fault diagnosis of analog circuits. The diagnostic results show that the proposed method improves the diagnostic accuracy by 5.5% compared with other algorithms.