Existing methods fail to effectively leverage check-in information to provide precise location recommendation services. To address this problem, this study introduces a novel model for the next point-of-interest (POI) recommendation based on dual-granularity sequence fusion. Firstly, the model integrates fine-grained spatio-temporal sequence information with naturally occurring coarse-grained categorical sequence information in real life. It effectively captures long-term dependency relationships using gated recurrent units to enrich the context of check-ins. Subsequently, the model uses the extracted information to transform the “hard” segmentation of long sequences into a “soft” segmentation, enabling the extraction of complete semantic information from local sub-sequences. Finally, the recommendation model aggregates salient information from each local sub-sequence. Experimental results on the Foursquare and Gowalla datasets show that the proposed model improves the recall by 9.07% and 9.37%, respectively, and enhances the normalized discounted cumulative gain by 9.72% and 10.24%, respectively. These results indicate that the proposed model exhibits superior recommendation performance.