Efficient retrieval is one of the crucial services of digital library, and sorting is the core issue of efficient information retrieval. Given a list of candidate book titles, the sorting model is used to generate a sorted list of book titles. When learning-based sorting algorithms are applied in the filed of information retrieval, they often optimize the sorting model by minimizing the value of the pairwise loss function. However, existing analysis shows that minimizing the pairwise loss value does not necessarily lead to the optimal sorting performance of the listwise algorithm. It is also very difficult to combine the online learning sorting algorithm with the listwise algorithm. This paper proposes an online learning sorting algorithm based on listwise, which aims to realize the online learning sorting algorithm under the premise of ensuring the performance advantage of listwise algorithm, thereby reducing the retrieval complexity. Firstly, the problem of combining the online learning sorting algorithm with the listwise algorithm is solved. Secondly, the sorting model is optimized by minimizing the loss function based on the predicted list and the real list. Finally, an adaptive learning rate based on the online-listwise algorithm is proposed. Experimental results show that the proposed algorithm has better retrieval performance and speed.