Current image matching algorithms mainly use the distance information between pixels to achieve feature matching, ignoring the variance information between images, resulting in more false matching in the matching results. An image matching method is proposed based on Laplacian feature constrained coupling variance measure. Firstly, Harris operator is introduced to extract image features roughly. On the basis of rough extraction, Laplacian feature of pixels is utilized to optimize the extracted image features in order to obtain more accurate image features. Then, the gradient feature of the image is employed to calculate the direction information of the image. Based on the gradient feature, the neighborhood of the feature points is established, and the Haar wavelet value in the neighborhood is solved to obtain the feature vector. Finally, the regional variance model is adopted to measure the variance information of the image, and it is introduced into the process of image feature matching. The variance information is added on the basis of Euclidean distance measurement of feature points to achieve image feature matching more accurately. Random Sample Consensus(RANSAC) method is adopted to purify the results of feature matching, eliminate mismatching and complete image matching. The experimental results show that compared with the existing matching algorithms, the proposed algorithm has better matching performance and higher accuracy, with accuracy above 90%.