Multi-View learning is a method to improve the robustness and learning performance of single-view learning by fusing the complementary information. Canonical correlation analysis (CCA) which is used to analyze correlation between two datasets of the same objects is an important method for multi-view feature fusion. CCA aims to seek a pair of projections associated with the two sets of data such that they are maximally correlated. However, CCA results in constraint of the classification performance due to not utilizing the class information or ordinal information of different classes for some applications in which the data labels are ordinal. In order to compensate such a shortcoming, ordinal discriminative canonical correlation analysis (OR-DisCCA) is proposed in this paper by incorporating the class information and ordinal information for extending the traditional CCA. The experimental results show that OR-DisCCA outperforms existing related methods.