The existing multiexposure fusion methods generally only determine the weight image according to the characteristics of each image. The fusion image has some problems, such as partial information loss and unclear details, especially during strong light in the image, producing nonideal fusion result. To solve these problems, a multiexposure image fusion method based on the entire sequence of image feature weights is proposed. First, the method determines the local brightness weight reflecting the importance of the brightness of each pixel in the image itself, the global brightness weight reflecting the importance of the brightness of each pixel in the whole sequence of the image, and the gradient weight reflecting the importance of the local gradient of each pixel in the whole sequence of the image; then the fused image is obtained according to the weight. Multiexposure image sequences containing various scenes were selected as the experimental data. The results show that the average multiexposure fusion structure similarity (MEF-SSIM) of the proposed method reaches 0.980, the average information entropy reaches 7.652, while the average running time is only 1.34 s. Compared with traditional methods and deep learning methods, the fusion image obtained by the proposed method has clear details, rich information, natural performance, more similar to the visual effect of the human eye, the fusion effect is better.