Straw fiber is a kind of huge renewable biological macromolecule material, and using crop straw as the raw material to manufacture plant fiber mulch is an ideal way of promoting comprehensive utilization of straw resource. Tensile strength of plant fiber mulch is a measure of damage caused by external stress. In order to accurately and effectively predict the tensile strength, reduce production cost and improve the utilization rate of raw materials, based on pilot-production line of plant fiber mulch, particle swarm optimization (PSO) used to optimize support vector machine regression (SVR) combined with the orthogonal test method (L25(56)) was proposed, namely, the PSO-SVR. The production processes variables were chosen, and the PSO-SVR model was established in Matlab 2011b. The input parameters affecting plant fiber mulch tensile strength through mechanism analysis were beating degree, dosage of wet strength agent, regulator, basis weight and mixture ratio;the evaluation index was tensice strength. The results were compared in terms of prediction accuracy with three prediction models respectively based on support vector machine regression (SVR), back propagation neural network regression (BP) and radial basis function neural network regression (RBF). The results obtained by using the PSO-SVR model showed that the mean square error was 0.117N2, the coefficient of determination was 0.915 and the root mean square error was 0.342N. The punishment factor and kernel parameter of SVR can select by PSO automatically. Compared with other intelligent algorithms, such as SVR, BP and RBF， PSO-SVR algorithm possessed superior applicability and stability. Therefore, this method can better reflect the actual tensile strength of plant fiber film, which can be used as a theoretical basis for the intelligent controlling under different process conditions.