In many real engineering applications, the distribution of training scenarios (source domain) and the distribution of testing scenarios (target domain) is different, thus the classification performance decreases sharply when simply applying the classifier trained in source domain directly to the target domain. At present, most of the existing domain adaptation methods are based on the probability-inference. For the problem of domain adaptation image classification, a collaborative representation based unsupervised method was proposed from the view of image representation. Firstly, all of the source samples were taken as the dictionary. Secondly, the three target samples closest to the target sample in the target domain were exploited to robustly represent the local-neighbor geometrical information. Thirdly, the target sample was encoded by combining the dictionary and the local-neighbor information. Finally, the classification was completed by using the nearest classifier. Since the collaborative representations have stronger robustness and discriminative ability by absorbing the target local-neighbor information, the classification method based on the new representations has better classification performance. The experimental results on the domain adaptation dataset confirm the effectiveness of the proposed method.