Objective: To achieve surface wear life prediction of abrasive blast rollers of grinding machines. Methods: The wear images of the grinding roller surface were acquired by the built image acquisition system, and the texture parameters such as second order moments, entropy value, contrast and correlation in the wear cycle of the grinding roller were obtained based on the grey scale co-generation matrix algorithm, and the obtained texture feature parameters were input into the constructed PSO-based LS-SVM algorithm model to finally predict the wear life of the blast roller. Results: The particle swarm algorithm could optimize the penalty factor and kernel parameters of LS-SVM well, and the PSO-LS-SVM algorithm was far superior to the LS-SVM algorithm model. The wear state of the blast roller surface of the mill could be accurately identified using the PSO-LS-SVM algorithm. Conclusion: The system can accurately predict the service life of the blast rollers.