Image and sentence matching remains challenging due to the large visual-semantic discrepancy. This mainly arises from two aspects: 1) images consist of unstructured content which is not semantically abstract as the words in the sentences, so they are not directly comparable, and 2) arranging semantic concepts in different semantic order could lead to quite diverse meanings. In this work, we propose a semantic concepts and order learning framework, which can improve the image representation by first predicting semantic concepts and then organizing them in a correct semantic order. Given an image, we first use a multi-regional multi-label CNN to predict its included semantic concepts in terms of object, property and action. Then, to organize these concepts, we use a context-modulated attentional LSTM to learn the semantic order. It regards the predicted semantic concepts and image global scene as context at each timestep, and selectively attends to concept-related image regions in a sequential order. To further enhance the semantic order, we perform an additional sentence generation. We learn the sentence representation with a conventional LSTM, and then jointly perform image and sentence matching and sentence generation for model learning. Extensive experiments demonstrate that our model can achieve the state-of-the-art results on two public benchmark datasets.