In the era of the Internet, all people's behaviors on the Internet will be stored in the server in the form of data, which leads to the image level growth of data today. In the context of big data, how to effectively analyze various existing data to obtain the necessary information is an urgent challenge that various industries need to overcome. Recommendation algorithms are one of them, mainly using existing data to recommend information of interest to users. In traditional recommendation algorithms, collaborative filtering recommendation algorithms encounter difficulties such as cold start and data sparsity. In order to better explore data features, deep learning algorithms have begun to be applied. Recurrent neural networks can not only learn input data but also perform self-learning, which can better extract features between data and improve the accuracy of recommendations. However, the information that users are interested in is greatly influenced by time, in order to improve the accuracy of recommendations. This study investigates the recursive neural network recommendation algorithm with added time series, and experiments have shown that this recommendation algorithm can indeed improve the accuracy of recommendations more accurately.