It is complex to design facade skin for different building, in view of the high operational energy consumption that accompanies buildings with excessively large window-to-wall ratios. For public buildings with, the energy load cost relatively large. There are a characteristic of facade which have high wall window rate to consume a lot of energy or increase Insulation cost due to the influence of interfaces. For the treatment of shading in summer, excessive overhang of the eaves often increases the load and structural cost of the roof or increases the external sunshade structure to increase structural cost. Integration of surface energy at the interface level, combined with the shading construction of light materials. In the current process of shortening the carbon cycle of buildings, study are exploring innovative exploration ways, focusing on building integrated sunshade and insulation for building with high window-wall-ratios. study consider the changes between winter and summer differentiated needs to propose optimized design solutions for reducing cooling and heating energy consumption under photo-thermal comfort conditions.set hina xian project as the case. Study show by new toughness skin design improving Performance and efficient cut of Energy cost. Solving photo-ermal objective analysis and selection problems based on ANN neural network learning prediction feedback, bench marking of environmental parameters, and parameter definition of evolutionary solvers. The results show that the solver can reach convergence at an early stage, and the validation of the chosen solution proves the effectiveness of the strategy-guided morphology. Based on learning prediction can be more accurately coupled with existing simulation trends. The total annual energy consumption of design scenario for this case skin, which is 8.4% more energy efficient than the conventional scenario and more than 5.3℃.