Rolling force prediction model is studied. Use artificial fish swarm optimization algorithm for SVR parameters selection of the optimal parameters combination and the particle swarm optimization algorithm is introduced to conventional artificial fish swarm algorithm

and it is improved

to improve the performance of the artificial fish swarm algorithm. Research results show that the rolling force calculation results error of Ekelund model are larger than 10 %. The rolling force prediction precision of conventional SVR forecasting model is less than 10 %

and the rolling force error obtained by the improved SVR prediction model is lower than 5 %. Through the artificial fish swarm algorithm

the parameters of the SVR algorithm model can improve the prediction precision of the prediction model

and the consumption time is the shortest in the three prediction models.