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Ultrafine grained (UFG) pure titanium was prepared by ECAP up to four passes. The hot compression tests were conducted in the different temperatures (250~450 ℃) and the strain rates of 10_-5~1s_-1.The artificial neural network (ANN) and Arrhenius constitutive equation were used for establishing constitutiveSmodel of UFG pure titanium, respectively. The experimental results show that the flow stress increased with the increase of strain at the beginning of the deformation, then increased slowly. Finally, the stress reached a stable value. The experimental value and the predicted value of flow stress showed that the average absolute relative errors obtained from the artificial neural network model and Arrhenius constitutive equations were 2.1% and 11.54%, respectively. The correlation coefficient of the artificial neural network model and Arrhenius constitutive equations were 0.9979 and 0.9464, respectively. It means that the artificial neural network model can more accurately describe the constitutive relations of UFG pure titanium. By comparing the error of the two models under different temperatures, it can be find that artificial neural network model has better stability under the condition of high temperature.