Screening potential formulations from numerous medicines is a critically important task in the field of Traditional Chinese Medicine (TCM), which is typically completed by experienced physicians based on the patient’s symptoms and the properties of different Medicines. However, the complex mechanism of action of TCM makes this task very challenging. To overcome these hurdles, research on TCM formulations has shifted towards target-based methods inspired by evaluation methods used in Western medicine, aiming for gaining a deeper understanding of TCM and its potential efficacy. Nevertheless, TCM has more action targets compared to Western medicine, which often leads to computational bottlenecks. Traditional machine learning-based methods can significantly reduce computational time, but they are less interpretable and more prone to overfitting. To this end, this paper proposes an efficient and accurate framework for screening TCM prescriptions. Specifically, we start by identifying the key targets for the specific disease and analyzing the interaction relationships between these targets. We then utilize a Graph Convolutional Network to extract community relationships between the targets and build a trustworthy hypergraph based on this information. Using this structure, we obtain a prescription representation and train a prescription evaluation network to learn the merits of existing TCM prescriptions. Finally, we evaluate our proposed method on two common chronic diseases in clinical practice, Parkinson’s disease and chronic gastritis, and the results demonstrate the effectiveness of proposed method in TCM prescription screening and evaluation.