Maximum mean discrepancy is merely used to reflect the overall distribution information and the global structure information of sample space, which neglects the difference of contribution of each sample to the global measure. In this paper, a kind of maximum distribution weighted mean discrepancy (MDWMD) is proposed where the whitened cosine similarity is used to design distribution weights for all samples from the source and the target domains. Thus, the distribution discrepancy information of each sample can be reflected in the global measure. Further, based on the MDWMD measure and the idea of joint distribution adaptation (JDA), a kind of domain adaptation learning algorighm called JDA-MDWMD is proposed. The JDA-MDWMD can simultaneously adjust the marginal probability distribution and the conditional distribution of the source and the target domains. Experimental results show that, compared with the typical transfer learning and non-transfer learning algorithms, the JDA-MDWMD yields higher classification accuracy on different types of cross-domain image datasets.