In　response　to　the　challenges　of　assessing　the　potential　for　demand　response　due　to　the　difficulty　in　observing　residential　users＇　internal　private　information　and　the　lack　of　historical　demand　response　data,　a　method　for　assessing　the　potential　of　residential　user　demand　response　based　on　Stackelberg　game　theory　derivation　and　improved　multi-task　learning　is　proposed.　First,　a　Stackelberg　game　theory　derivation　model　is　established　between　the　electricity　retail　company　and　the　residential　user＇s　home　energy　management　systems　to　address　the　modeling　issues　of　residential　user　demand　response　behavior　under　incomplete　information.　This　model　uses　the　equilibrium　of　virtual　games　to　mine　the　electricity　demand　price　elasticity　coefficients　from　price-volume　information　and　extracts　demand　response　characteristic　parameters.　Secondly,　a　multi-task　learning　model　establishes　a　mapping　relationship　between　electricity　usage　characteristics　and　demand　response　characteristic　parameters.　A　gradient　normalization　algorithm　is　employed　to　address　the　issues　of　inconsistent　gradient　magnitudes　and　mismatched　convergence　speeds　among　subtasks.　This　facilitates　the　generalized　modeling　of　demand　response　characteristics　for　the　residential　user　group,　thereby　enabling　the　assessment　of　the　demand　response　potential　of　this　group.　Finally,　the　proposed　method　is　validated　using　residential　user　data　from　Northern　Ireland,　demonstrating　its　superiority　in　mining　demand　response　characteristics　and　assessing　demand　response　potential.　©　2025　Power　System　Technology　Press.　All　rights　reserved.