Aiming at the issues of long time-consuming and low efficiency in traditional numerical simulation methods for optimizing thermal parameters of building envelopes, which are difficult to meet the timeliness requirements of engineering projects, this paper initially employs EnergyPlus energy consumption simulation data to construct a building energy consumption prediction model on the basis of a BP neural network,and then provides recommended values for various thermal parameters of the envelope using the building energy consumption prediction model combined with a multi-objective optimization model. The research results show that the building energy consumption prediction model, incorporating thermal parameters of roof, wall, floor and window, exhibits a high degree of fit, with an average coefficient of determination (R2 ) of 94.98% and an average coefficient of variation of root mean square error (CVRMSE) of 4.42%. Compared with the case building, the optimal parameter combination, as predicted by the BP neural network energy consumption prediction model and multi-objective optimization model, reduces energy consumption by 38.7%, while the initial investment increases by 251 thousand yuan, representing a 15.1% increase.