At present, the processing and analysis of optical remote sensing images is mostly concentrated on the detection and recognition of single target. The multi-target detection and recognition has become a very important research topic. A multi-target detection and recognition algorithm for optical remote sensing images was proposed. Firstly, the adaptive threshold algorithm was used for target detection and segmentation quickly. Then, a hierarchical BoF-SIFT (Bag of Feature-Scale Invariant Feature Transform) feature was constructed by effectively combing the image pyramid and BoF-SIFT feature to represent the global features and local features of the target and describe the distribution characteristics of the target in detail. Finally, the support vector machine based on radial basis function was used as weak classifier for AdaBoost algorithm. Then a strong classifier was obtained to complete classification and recognition of the target image after continuous updating weights. The recognition rate reached 93.52%. A large number of experimental results show that the proposed algorithm has a notable effect on the segmentation of multi-class target in remote sensing images. The feature is selected appropriately and the recognition method is fast and effective.