The friction effect and temperature rise of deformation in hot compression experiment could lead to the inaccuracy of the measured flow stress, and when the conventional flow stress is corrected, the stress difference caused by temperature rise is simplified as a linear function of temperature, which has a large error. Therefore, an artificial neural network (ANN) model considering the friction effect and temperature rise of deformation was established, and the flow stress curve of TC18 titanium alloy in the β-phase region was obtained by thermal simulation experiment machine Gleeble-3500. Based on the friction correction method and the calculation method of temperature rise due to deformation, the data of flow stress and deformation temperature were corrected and used as the training set of the ANN model, and the ANN model with integrated friction-temperature rise correction was obtained. The comparison between the experimental and predicted results by the ANN model reveals that the friction effect and temperature rise of deformation have a significant influence on the flow stress, and the flow stress curves predicted by the ANN model are more consistent with the microstructure, which indicates the accuracy of the ANN model. Furthermore, a power dissipation diagram was established based on the ANN model prediction results, and the microstructure evolution and processing performance of the alloy during hot deformation wrere analyzed in combination with microstructure characterization. The power dissipation factor η is significantly affected by the deformation temperature and strain rate, the highest value of η is found at 950-990 ℃ and 0.01-0.001 s-1, at which the dynamic recovery and recrystallization of the alloy take place, and the hot processing performance is the best. Thus, the research results provide a reference for the correction method of flow stress, and are of great significance for the formulation of hot processing process parameters for TC18 titanium alloy in β-phase region.