Electronic health records (EHR) store rich data of medical entities, such as diagnoses, procedures, and medications, which are invaluable in the development of automated systems for hypertension medication recommendations. These entities within EHR demonstrate significant synergies during the treatment process. However, existing medication recommendation methods predominantly focus on homogeneous graphs, thus overlooking the crucial synergistic relationships among heterogeneous medical entities. Moreover, accurately modeling the progression of hypertension using EHR is essential for precise medication recommendations, but current approaches often lack comprehensive temporal modeling and do not fully meet clinical requirements. To overcome these challenges, this paper introduces a novel model for hypertension medication recommendation that leverages the synergy and selectivity of heterogeneous medical entities. Initially, patient EHRs are utilized to construct both heterogeneous and homogeneous graphs. The inter-entity synergies are then captured using a multi-head graph attention mechanism, which enhances the entity-level representations. Subsequently, a dual-layer temporal selection mechanism calculates selective coefficients between current and historical visit records, thereby aggregating these to form refined visit-level representations. Ultimately, medication recommendation probabilities are determined based on these comprehensive patient representations, yielding practical and actionable recommendations. Experimental evaluations conducted on the real-world dataset MIMIC-IV v2.2 demonstrate that our model significantly outperforms baseline models. It achieves a Jaccard similarity coefficient of 55.82%, a precision-recall AUC of 80.69%, and an F1 score of 64.83%, thereby demonstrating its superior efficacy in medication recommendation. These results underscore the potential of our model to enhance clinical decision-making in the management of hypertension.