Aiming at the problem of low image matching rate under single feature condition, and the uneven extraction of feature points of the scale-invariant feature transform (SIFT) algorithm due to fixed contrast threshold, we propose a novel image matching method based on adaptive threshold prediction under hybrid features. Firstly, the algorithm uses the SIFT to extract image feature points. Then, we employ the texture parameter second moment method to adaptively calculate the optimal threshold, and the descriptive texture feature vector to constrain the SIFT matching process. Experimental results demonstrate that the proposed method can adaptively select the contrast threshold according to the gray level distribution of the image, enhance image detail information and stabilize the number of extracted feature points. The texture vectors constrain the matching process to avoid the mismatch of similar regions. The method is robust to illumination and blurred images.