User mobility behaviors frequently exhibit a spatial clustering phenomenon, wherein points of interest (POIs) visited by the same user tend to be in close proximity. Consequently, leveraging geographical influences for user preference modeling remains a prevalent approach in POI recommendation tasks. However, existing studies often overlook users’ hidden geographical habits for the following reasons: (1) Geographical features are commonly approximated by manually partitioned regions or fixed distributions, inadequately capturing the nuanced spatial proximity among POIs. (2) POIs with high geographical correlations are not explicitly incorporated as feedback signals during the training process, resulting in a lack of spatial clustering pattern learning within users’ preference representations. This paper introduces GeoCo, aGeographicalCorrelation enhanced network for POI recommendation. First, we model POIs’ geographical features using fine-grained hierarchical sequences to capture multilevel spatial relations. Subsequently, we propose a pre-training network that employs the sentence similarity assessment technique to comprehend the semantics of geographical correlations. Second, we introduce a novel multi-objective training process that intuitively learns spatial clustering patterns through user mobility behaviors. Extensive experiments conducted on two location-based social network (LBSN) datasets, Gowalla and Foursquare, demonstrate the superiority of our proposed model over fourteen state-of-the-art baseline models in POI recommendation tasks. Compared with the baselines, GeoCo has achieved a performance improvement of at least 5$\%$in Rec@5 and HR@5 on both datasets. Furthermore, we verify the effectiveness of pre-trained location vectors and the multi-objective training process in enhancing the model's understanding of geographical correlations for user preference construction.