The forged Ti6242s titanium alloy was subjected to a thermal compression simulation experiment with 75% deformation at a temperature of 950~1010℃ and a strain rate of 0.01~10s-1 by Gleeble-3800. Based on the true stress-true strain curve obtained from the experiment, the artificial neural network (ANN) and Arrhenius equation were used to establish the constitutive model of Ti6242s alloy, and study its thermal deformation behavior. The results show that the flow stress rapidly rises to the peak stress after the deformation begins, and then the hardening and softening reach a dynamic balance. After the true strain reaches 0.6, the work hardening gradually dominates, and the hardening amplitude increases with the increase of the strain rate; artificial neural network The average relative error (AARE) of the predicted value of the constitutive model is 2.5%, and the correlation coefficient (R) is 0.999; the AARE of the predicted value of the Arrhenius equation constitutive model is 14.5%, R is 0.955, and the accuracy fluctuates greatly within the parameter range; The accuracy of the ANN constitutive model is much higher than that of the Arrhenius constitutive model, and it has consistent accuracy across the entire parameter range; the ANN constitutive model has good generalization ability, and it still has high accuracy in predicting flow stress outside the range of experimental parameters.