Circular sawblades with large diameter-thickness ratios feature weak stiffness which results in self-excited and forced vibrations in high-speed sawing. The scenario brings uncertainty to the evolution of the multichannel signal features, which sometimes even exhibit contradictions, making it extremely challenging to predict the sawblade lifespan. To fill the gaps, a transverse vibration differential equation was firstly established to elucidate its complex behavior. Then, this study used an artificial intelligence algorithm based on multidomain features to predict the condition of the sawblade. Therefore, a hybrid prediction model was constructed using a BP neural network (BPNN) optimized by a genetic algorithm (GA). Cutting experiments were carried out to prove that the proposed model can effectively predict the sawblade condition. This study not only provides useful insights for the condition monitoring of sawblades, but also offers a solid foundation for monitoring wood, stone, and composite processing.