Objective The existing parameter optimization methods generally have problems such as large time cost large memory occupation difficulty in solving high-dimensional data and difficulty in finding global optimal solutions. The DYCORS algorithm can find the global optimal solution for high-dimensional data problems even with the saving of time cost and memory. Therefore in view of the problems existing in the existing parameter optimization methods the YDYCORS algorithm for block optimization of OVA-SVM model parameters was proposed. Methods Among the parameters of OVA-SVM the penalty parameter C the kernel type k the RBF kernel function parameter γ the ploy kernel function parameter d and the iteration termination parameter t have a greater impact on the model. Due to the large computational effort of adjusting five parameters simultaneously it is difficult to find the optimal solution. The DYCORS algorithm can also be applied to high-dimensional data problems by reducing the number of iterations and then the parameters were adjusted in blocks based on the DYCORS algorithm. The most influential parameters C k and γ were adjusted first then the optimal parameters C k and γ were fixed the more influential parameters d and t among the remaining parameters were adjusted and finally the five optimal parameters that had been obtained were adjusted simultaneously so that the parameters were adjusted in blocks to improve the effect of parameter optimization. Results Through the comparison of the experimental results on MNIST and IRIS data sets it can be found that after using the YDYCORS algorithm to adjust the parameters of OVA-SVM in blocks the model accuracy can be higher than the accuracies of manual parameter adjustment and directly using DYCORS to adjust the five parameters at the same time which can also further improve its model performance. Conclusion The final experimental results show that DYCORS algorithm can effectively solve the problems of OVA-SVM parameter optimization such as high time cost large memory occupation difficulty in solving high-dimensional data problems and difficulty in finding the global optimal solution. In particular the improved YDYCORS algorithm can further improve the accuracy of the OVA-SVM model and obtain a better model effect.