The work aims to propose an analysis method based on deep learning to predict the mechanical properties of mild steel, in order to analyze the mechanical properties of mild steel under large plastic tensile load in mechanical engineering. Firstly, the tensile experiments were carried out to the mild steel materials with different step angles and the collected experiment data were analyzed by intelligent technology. The experiment model was designed to be a two-layer structure. The first layer was shared full connection layer for feature input. The second layer adopted extreme random tree and long-term and short-term memory network to carry out parallel depth training, and output the training results after activation function calculation. The parallel training model could better reflect the strain and stress change trend, velocity and numerical relationship in the experimental test set. The experimental results showed that the parallel training model could improve the fitting effect by 28.3% and 63.5% respectively compared with the single ET and LSTM prediction technology. Good prediction results can be obtained through the new model, which provides an important reference for the analysis of mechanical properties of mild steel materials in the metal damper under large plastic tensile load.