Objective To predict the constitutive parameters of a superelastic model of plantar soft tissues based on random forest (RF) and backpropagation (BP) neural network algorithms to improve the efficiency and accuracy of the method for obtaining constitutive parameters. Methods First, a finite element model for a spherical indentation experiment of plantar soft tissues was established, and the spherical indentation experiment process was simulated to obtain a dataset of nonlinear displacement and indentation force, divided into training and testing sets. The established RF and BP neural network (BPNN) models were trained separately. The constitutive parameters of plantar soft tissues were predicted using experimental data. Finally, the mean square error (MSE) and coefficient of determination (R2) were introduced to evaluate the accuracy of the model prediction, and the effectiveness of the model was verified by comparison with the experimental curves. Results Combining the RF and BPNN models with finite element simulation was an effective and accurate method for determining the superelastic constitutive parameters of plantar soft tissues. After training, the MSE of the RF model reached 1.370 2×10-3, and R2 was 0.982 9, whereas the MSE of the BPNN model reached 4.858 1×10-5, and R2 was 0.999 3. The inverse-determined constitutive parameters of the plantar soft tissues suitable for simulation were obtained. The calculated response curves for the two predicted sets of constitutive parameters are in good agreement with the experimental curves. Conclusions The prediction accuracy for the superelastic constitutive parameters of plantar soft tissues based on an artificial intelligence algorithm model is high, and the relevant research results can be applied to study other mechanical properties of plantar soft tissues. This study provides a new method for obtaining the constitutive parameters of plantar soft tissues and helps to quickly diagnose clinical problems, such as plantar soft tissue lesions.