Considering that the velocimetry accuracy will decline when the optical flow information is affected by foreground moving objects and noise in UAV visual navigation, we proposed an optical flow velocimetry optimization method based on the Mean-Shift algorithm.The method uses the pyramid Lucas-Kanade algorithm to calculate the sparse optical flow and the improved Mean-Shift algorithm for the clustering of the optical flow data, which eliminates the interference items in the data according to the consistency of speed.Experimental results show that the proposed method can reduce the effects of foreground moving objects and noise on optical flow velocimetry in UAV visual navigation, and improve the precision of UAV velocity measurement.