The extraction of vision key-points decides directly cognitive navigation efficiency for UCAV. In order to extract high robust key-points for UCAV, an algorithm named multi-quantifying scale invariant feature transform (MQ-SIFT) with key-points optimization is proposed. According to the deficiency of SIFT algorithm in analogue feature vectors' balance and correct matching score, a method combining the multiple value quantifying and reshaping operation is presented to quantify analogue feature vectors. The analysis and simulation results verify the better properties of this method. Furthermore, in order to perfect the property of MQ-SIFT with fewer robust key-points, the optimization rules are discussed, and an iterative cross-Euclidean distance search method is proposed to search the maximum connected set. Simulation results show that MQ-SIFT algorithm has higher correct matching score with signal-to-noise (SNR) above 10 dB, and their matching score can meet the requirements of cognitive navigation system.