To address the needs for the rapid modal frequency prediction and structural fault localization for slender body aircraft，an efficient method combining engineering prior knowledge，artificial neural networks，and parameter identification is proposed.Based on the sample design，the one-dimensional mass-spring-stiffness beam model is used to calculate the low-order modal frequencies of the structural dynamics of a slender body aircraft.By leveraging the rapid mapping capability of neural networks，the global mass and stiffness of the aircraft are efficiently identified based on the modal frequencies achieved.In engineering practice，the nose cone mass anomalies are accurately located，and the quality error does not exceed 3.13%.