In order to solve the difficult problem of weak fault feature extraction of rolling bearing, a fault diagnosis method of rolling bearing based on acoustic emission characteristic parameters and waveform flow analysis was proposed, based on waveform analysis of acoustic emission signal under complex transmission path. Firstly, the state of the rolling bearing was preliminarily judged by the acoustic emission characteristic parameter TAFI (time arrival feature index). Secondly, the fault diagnosis of rolling bearing was carried out by using experience graph analysis and distribution graph analysis. Finally, the waveform flow of acoustic emission signal of faulty bearing was screened and reconstructed by kurtosis criterion to extract fault information. In order to verify the effectiveness of this method, a rolling bearing fault simulation test and an aero-engine intermediate bearing simulation test were carried out, and the acoustic emission signals of typical rolling bearing faults were obtained, and the data were processed and analyzed by the established method. The results show that the TAFI image of acoustic emission characteristic parameters presents regular bars, which can preliminarily determine that the rolling bearing is in fault state. The impact number of energy, count value, amplitude and count of the faulty bearing is higher than that of the healthy bearing, which can effectively identify the fault characteristics of the rolling bearing. The typical faults of rolling bearing can be judged by analyzing acoustic emission waveform flow.