The multi-target detection and recognition of optical remote sensing images have always been the hot researching topics in image processing and analysis. The multi-target classification and recognition algorithm based on multiple features and single classifier cannot make good use of the adaptability of features and classifiers, resulting in a problem that the accuracy of recognition is difficult to improve. A multi-feature multi-classifier fusion optical target image recognition algorithm based on D-S evidence theory is proposed. Two features with translation and scaling invariance are extracted. Secondly, three classifiers are introduced to classify the feature. Finally, a two-level fusion algorithm scheme by using D-S evidence theory is proposed, and a confusion matrix that characterizes the performance of the classifier is introduced in the calculation process of confidence function. The proposed algorithm is effectively resolved the classifier output uncertainty problem, and further improves the accuracy of multi-target classification and recognition of optical remote sensing images. The recognition rate of multi-objectives by DS evidence theory fusion strategy reaches 97. 22%. The effectiveness of the algorithm is proved.