Prescription recommendation is critical for clinical decision support in Traditional Chinese Medicine (TCM), aiming to recommend a herb set based on a patient's symptoms. The core principle of TCM clinical practice, treatment based on syndrome differentiation (SD), follows a four-step progressive process: symptoms to syndromes, therapeutic methods, and herbs. However, existing models oversimplify this process by overlooking therapeutic methods, directly mapping symptoms to herbs or syndromes to herbs, resulting in information loss and reducing the effectiveness of recommended prescriptions. Furthermore, the implicit, sparse, and many-to-many relationships between syndromes and therapeutic methods, coupled with the nonlinear interactions between therapeutic methods and herbs, further hinder the modeling of the complete SD process. To address these challenges, we propose a novel four-partite graph paradigm that explicitly models the four key components of SD and their interactions, preserving critical information at each step and aligning more closely with clinicians' decision-making logic. Building on this, we develop SDPR, an SD-based prescription recommendation model comprising four modules aligned with all SD steps. Then, we integrated them into a multi-task learning framework to fully capture the progressive prescription process. To handle the implicit and complex relationships among syndromes, therapeutic methods, and herbs, we introduce a syndrome-induced pre-training strategy and a therapeutic method-aware contrastive learning framework. Extensive experiments on public and real-world datasets validate SDPR's effectiveness in herb recommendation and prescription retrieval, confirming the strength of the four-partite graph paradigm. Our broader goal is to advance the intelligent development of TCM in healthcare.