Synthetic aperture radar (SAR) target recognition networks performance has been remarkably improved, posing serious exposure risks to our high-value targets. Researches have shown that it is valid to protect our high-value targets by generating adversarial examples. However, most existing SAR adversarial examples generation approaches are based on the premise that irregularly global perturbation data can be directly added to SAR images, which is difficult to implement in practice. To this end, a target partial-occlusion SAR adversarial examples generation approach is proposed in this paper. First, the target region in SAR image is extracted using the combination of OTSU algorithm and morphology operations. Then, the random search (RS) algorithm is introduced to optimize the occlusion position in the extracted target region with the constraint of occlusion area and value, so as to misclassify the SAR target recognition networks. Experimental results based on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown the effectiveness of the proposed method.