To study the influence of geometric characteristic parameters and forming process parameters of automobile stamping die on the sharp-edged wear and realize the accurate prediction of the die sharp-edged wear， a prediction method of the die sharp-edged wear based on improved SVR algorithm was proposed in this paper. By using the improved Latin hypercube sampling （ILHS） method， the experimental samples of finite element calculation of die sharp-edged wear were obtained， and the input parameter set of the prediction model was then constructed. The chaos theory and dynamic weights were introduced into the grasshopper optimization algorithm （GOA）， and the improved grasshopper optimization algorithm （IGOA） was used to improve key parameters of the SVR algorithm. Based on the IGOA-SVR algorithm， the prediction model of die sharp-edged wear was constructed， which was combined with the particle swarm optimization （PSO） algorithm to establish a multi-objective optimization model so as to realize the high-precision prediction as well as the optimization of geometric characteristic parameters and forming process parameters. Compared with five existing conventional prediction models， the prediction errors of the prediction model based on IGOA-SVR at the sampling point were 8.546%， 8.497%， and 8.473%， respectively， which were 25.9%， 26.2%， and 26.4% higher than the GOA-SVR prediction model， respectively， and the prediction accuracy was also improved to varying degrees compared with other prediction models. The results show that the improved IGOA-SVR has higher accuracy.