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Analysis on the influence of deformation temperature, strain rate and deformation degree on the microstructure after solution treatment was carried out, and a predicting model for the recrystallization volume and average grain size of the microstructure was established by a three-layer feed-forward artificial neural network with a back-propagation learning rule. The results indicate that under the condition of the same cooling and heat treatment rules, the deformation parameters such as deformation temperature, deformation degree and strain rate have important influence on the microstructures evolution after hot deformation and solution treatment of TB8 titanium alloy. A larger deformation degree, a lower temperature and an appropriate strain rate are required to acquire the microstructure with homogeneous and fine grains. The close agreement of the predicted results with measured ones shows that the neural network is able to successfully predict the variation of the microstructure with the hot deformation parameters. The above results can provide the determining of reasonable hot forming process with a scientific base.