A semi-supervised Adaptive Boosting(AdaBoost) model tree based modeling approach is proposed for Field Programmable Gate Array(FPGA) performance characterization. The proposed approach, which adopts AdaBoost to improve the prediction accuracy, constructs an analytical performance model with regard to the FPGA architecture parameters in semi-supervised learning way. The FPGA performance model built through the proposed approach estimates the area, delay and area-delay product with Mean Relative Errors(MREs) of 4.42%, 1.62% and 5.06%, respectively. Compared to the supervised model tree and the previous semi-supervised model tree algorithm, the proposed approach boosts the estimation accuracy by 39% and 26% respectively. Experimental results show that the proposed approach is proved to be an efficient FPGA characterization approach, building FPGA performance models with high accuracy in less time cost. The proposed modeling approach can be applied to explore the FPGA architecture design space effectively and efficiently.