•The aluminum alloy 6082 in this study is an extruded material after aging.•Compare the traditional constitutive equation of the material and the strain-compensated Arrhenius model with the newly established PSO-BP network model.•Use statistical analysis methods for data analysis. It is important to model the flow behavior of an aged aluminum alloy AA6082 as an extrusion material before design and optimize of the forming process. In this study, the isothermal compression tests were carried out on Gleeble-3800 thermal simulator in the temperature range of 423−773 K and the deformation condition of 0.01–1.0 s−1 to study the cold temperature and hot deformation behavior of aluminum alloy AA6082. Considering the experimental error, the discussion based on friction and temperature correction is carried out. According to the modified data, based on the traditional Arrhenius constitutive model, a method combining the strain compensation Arrhenius model and PSO-BP neural network was proposed to describe the flow behavior of aluminum alloy AA6082. The prediction ability and stability of the model are evaluated by using the linear correlation coefficient(r), the average relative errors (AARE), the Root mean square errors(RMSE) and the relative errors (RE) in statistical analysis. The results show that the 3-10-8-1 double hidden layer neural network model based on PSO-BP has higher effect in predicting the flow characteristics of aging aluminum alloy AA6082 than that of Arrhenius model based on strain compensation. The linear correlation coefficient, mean relative error and root mean square error are 0.9996 %, 2.001 % and 1.665 MPa respectively.