In traditional recommendation algorithms, there is often a lack of consideration of users’ long short-term interest preferences. However, with the deepening of the application of deep learning in recommendation algorithms, this problem can be solved well. In response to the problem, this study proposes a recommendation algorithm based on long short-term interest preferences (RA_LST), which integrates a latent factor model and a gated recurrent unit. It can capture users’ long short-term preferences respectively and thus effectively solves the problem that the recommendation effect decreases due to users’ interest changing with time. The final experimental results show that the proposed algorithm improves the recommendation accuracy on different data sets.