Partial label learning is a weakly supervised learning framework, which attempts to select the only correct label from multiple candidate labels of the sample. Disambiguation is an important means in partial tag learning, which mainly uses algorithms to identify potential real tags. At present, researchers generally use a single feature space or label space for disambiguation, which is easy to lead the algorithm to fall into saddle point under the guidance of inaccurate prior knowledge. In order to solve the problem that similar samples are easy to be affected by abnormal samples in the process of disambiguation, sample outlier and outlier graph are defined. On this basis, a partial marker learning method for dissociation graph guided disambiguation is proposed. The algorithm uses the difference of label space to construct the divorced point map, which can effectively combine the prior knowledge of feature space and label space, and restrict each other to resist the potential high risk brought by divorced samples to the disambiguation process. Experimental results show that compared with PLKNN, IPAL, SURE, PL-AGGD, SDIM, PL-BLC and PRODEN, the proposed algorithm performs better in the partial label learning method and achieves good disambiguation effect.