Location-based social networks (LBSNs) contain a variety of heterogeneous information and are widely used for tasks such as POI recommendation and user relationship inference. However, existing models often overlook the sequential nature of POIs in user check-ins and fail to capture the long-term dependencies of POIs in the check-ins, resulting in suboptimal embedding quality. To address the above issues, this paper proposes a model for learning Sequential Features of check-ins for User Relationship Inference (SFURI), which aims to improve user embeddings. First, we utilize a Bidirectional Long Short-Term Memory network (BiLSTM) to learn the sequential information of POIs in the check-ins, which effectively captures users’ dynamical preferences for POIs. Second, we exploit an attention mechanism to learn the long-term dependencies of check-in times in the check-ins and a feedforward neural network (FNN) to optimize the global features of times in the check-ins, which effectively captures users’ temporal preferences for POIs. The SFURI model enhances user embeddings, resulting in better user relationship inference. Experimental results show that the SFURI model outperforms the baselines across all the three datasets, demonstrating its effectiveness in learning user embeddings.