Abstract: Muscle injury and other diseases often occurs in high-intensity physical workers, so the research on the deformation characteristics and the stress distribution of skeletal muscles are of increasing importance. It is important to obtain the correct constitutive parameters for the study of mechanical behavior of biological soft tissues, and the determination of the constitutive parameters is essentially an inverse process, which possesses challenges. In this paper, two inverse methods based on machine learning are proposed to determine the constitutive parameters, which are k-nearest neighbor (KNN) model and support vector machine regression (SVR) model combined with nonlinear finite element simulation. Firstly, based on the principle of nonlinear mechanics, a finite element model is established to simulate the nonlinear deformation of skeletal muscles under compression, and the corresponding deformation characteristics and stress distribution. At the same time, the dataset of nonlinear relationship between nominal stress and principal stretch of skeletal muscles is established by using the finite element model. Then KNN model and SVR model are used to build the machine learning intelligent algorithms for the inversion of constitutive parameters of skeletal muscle tissues, and the corresponding datasets are trained. Combined with the experimental data of uniaxial compression experiment, the constitutive parameters of skeletal muscles are predicted. Finally, intensive studies also have been carried out to compare the performance of KNN model with SVR model to identify the hyperelastic material parameters of skeletal muscles. And the validity of two inversion methods were verified numerically by introducing the correlation coefficient (R) and the decision coefficient (R^2). The results show that KNN model and SVR model combined with finite element method are effective and accurate method to identify the hyperelastic material parameters of skeletal muscles. This method can also be further extended for the predictions of constitutive parameters of other types of nonlinear soft materials.