Weakly supervised object localization (WSOL) aims to localize the entire and well-defined objects only via image-level labels for reducing the need of labor-intensive annotation and mitigating annotation errors. However, many WSOL methods via class activation maps (CAMs) often suffer from incomplete activation and inaccurate boundaries for object localization. In this article, we propose a novel multi-layer decoupling attention localization (MDAL) network to address these issues. We first present a simple yet effective multi-layer comparison decoupling mechanism including a maximum decoupling function and a minimum decoupling function to sufficiently activate and fuse multi-layer features. Then, we introduce the multi-layer maximum decoupling function into the attention modules, and develop a channel attention activation decoupling (CAAD) module and a spatial attention activation decoupling (SAAD) module, which can mine much more useful information for more possible regions' activation. Furthermore, the multi-layer minimum decoupling function is introduced to efficiently fuse and refine multi-layer features, which can suppress the over-activation and background noise. Finally, we develop a joint loss function to train the MDAL network. Experimental results on CUB-200-2011 and ILSVRC2012 demonstrate that our proposed network can provide accurate and complete object localization.