The thermal simulation compression experiments were conducted on forged TA16 titanium alloy using the Gleeble-3800 system at temperatures ranging from 730°C to 1030°C and strain rates from 0.1 to 10 s?1. The true stress-true strain curves of TA16 alloy under these deformation conditions were obtained. Constitutive models for the TA16 alloy were established using three different methods: the Arrhenius model, the Johnson-Cook model, and artificial neural networks (ANN). The model errors were analyzed. The results indicate that the TA16 alloy reaches a dynamic balance between work hardening and softening after yielding at medium and low strain rates. At high strain rates, it initially softens and then enters a balance state, demonstrating good workability. The mean absolute percentage error (MAPE) of the constitutive models for the TA16 alloy using the Arrhenius model, the Johnson-Cook model, and ANN were 11.49%, 23.7%, and 1.69%, respectively. The ANN model showed an order of magnitude higher accuracy compared to the traditional constitutive models. The Arrhenius model exhibited better accuracy at medium and high strain rates and in the medium and low strain range, making it practical for engineering applications. The Johnson-Cook model struggled to describe the dynamic equilibrium state after yielding for the TA16 alloy, resulting in poor model accuracy, making it unsuitable for engineering applications. The ANN model demonstrated extremely high predictive accuracy across the entire range of experimental parameters, and also maintained good accuracy for data predictions outside the experimental parameters, providing a highly accurate constitutive model for engineering practice.