The galvanized steel pipe is used as the research objective

and the thickness parameters of 167 steel pipes were used as the modeling data and validation data. The sensitivity evaluation factors included the lead-up speed

lead-out speed

draw time

discharge time

press-down time

zinc liquid temperature

zinc dipping time

external blowing pressure and wind ring position. A prediction model for galvanized coating thickness of steel pipes based on the SVM model was established

and a sensitivity analysis of the calculation model was carried out by using the Sobol method to determine the influence of the processing parameters on the galvanized coating thickness. Four swarm intelligence optimization algorithms were used to optimize the SVM model

the prediction accuracy was analyzed

and the optimal combination of zinc coating thickness parameters was given by searching the optimal model. The results show that wind ring position

zinc dipping time

zinc liquid temperature

press down time and extraction speed are important parameters that affect the prediction results of the model. The SVM model by using the Golden jackal optimization (GJO) has a fast convergence speed and good prediction ability

which is the best model among four models. The model of GJO-SVM is optimized by using the genetic algorithm to obtain the optimal processing parameters. For the practical production

under the requirement of the standard

zinc material can be saved and efficiency can be improved.