A dim-small target detection algorithm based on the classification of the background is presented to reduce the influence of the complexity of the background. The classification of the background is based on the complexity of the background. The complexity of background is estimated by the entropy of the image. And the lower-resolution wavelet transform idea is introduced in order to simplify the algorithm. Then the entropy of the image is mapped to the fuzzy plane as a background factor image by the fuzzy membership function. The complex background is separated from the flat sky background to realize the background classification through thresholding the background factor image with the maximum entropy threshold segmentation. Next the target is separately segmented by using the Robinson filtered image based on the two type of background characteristics to reduce the influence of the complexity of the background. The experiments show that this algorithm can reduce the interference of complexity of background for small objects, and it can improve the probability of the point target detection.