Traditional template matching methods suffer from heavy occlusion, intense background change and non-rigid deformation. A multi-scale saliency template matching method is proposed in this article in order to deal with such conditions. The method extracted saliency and multi-scale features in parallel. On the one hand, the template and the target images were first divided into grids of different scales using spatial pyramid model. Deformable Diversity Similarity (DDIS) was calculated under such different grids. On the other hand, saliency map of the template image was calculated using saliency segmentation method. Such saliency map s are then used to weight the scores calculated by DDIS under different grids. Finally, the final score map is calculated by fusing the score maps under different grids. The method proposed achieves 2.9% AUC(Area Under Curve) improvement compared with original DDIS method. Experiments show that salient object segmentation helps the method to focus more on object than background, therefore improve the robustness to background changes and occlusion. Besides, spatial pyramid model makes the method to consider information from different scale, for example, local contours and structural features of an object. Combining these two factors raises the matching accuracy significantly.