In online education systems, the courseware plays a pivotal role in helping educators present and impart knowledge to students. The originality of courseware heavily impacts the choice of educators, because the teaching content evolves and so does courseware. However, how to measure the originality of a courseware is a challenging task, due to the lack of labels and the difficulty of quantification. To this end, we contribute a similarity ranking-based unsupervised approach to measure the originality of a courseware. In particular, we first exploit a pre-trained deep visual-text embedding to obtain the representations of images and texts in a local manner. Next, inspired by the design of capsule neural network, a vector-based pooling network is proposed to learn multimodal representations of images and texts. Finally, we propose a Discriminator to optimize the model by maximizing the mutual information between local features and global features in an unsupervised manner. To evaluate the performance of our proposed model, we further subtly collect a dataset for evaluating the originality of courseware by treating sequential versions of each courseware as ranking lists. Therefore, the learning-to-rank scheme can be utilized to evaluate the similarity-based ranking performance. Extensive experimental results have demonstrated the superiority of our proposed framework as compared to other state-of-the-art competitors.