Health state monitoring of certain rotor portion in the aerospace engine is critical to the aerospace engine working normally, which effects launch task success further. Traditionally, health state monitoring depends on subjective experience and professional knowledge, so the corresponding health monitoring model is built after studying and understanding the failure mechanism behind. In view of the low efficiency, poor adaptability and over-reliance on expert experience of traditional rotor health state monitoring means, combined with massive amounts of rotor vibration health state monitoring historical data, neural network BiSNet is proposed and designed as an artificial intelligent method to model the certain rotor portion in the aerospace engine health monitoring model and monitor the health status timely. Compared with the traditional monitoring method and the mainly general baseline intelligent method, the conclusion is drawn that the historical data-driven intelligent neural network BiSNet could provide convenient and accurate modeling and prediction for the health monitoring of aerospace rotors.