As an important therapeutic resource, traditional Chinese medicine (TCM) has undergone thousands of years of clinical practice and application. To promote the modernization of TCM and explore its application potential in new indications, this study draws on research experience from drug repurposing in Western medicine and combines emerging network medicine theories to propose two random walk-based models for predicting potential therapeutic associations between TCM and symptoms: M-RW and GO-DREAMwalk. The two models incorporate path-based and functional information between TCM and symptoms to guide the random walk process. The resulting node sequences are input into a heterogeneous Skip-gram model to learn the embedded vector representations of nodes. Subsequently, an XGBoost classifier is trained by adopting TCM-symptom association labels and the learned embedded vectors. Finally, the models are tested and evaluated by employing clinical data on liver cirrhosis. In the clinically effective prediction task, the top-ranking prediction precision of the two models reaches 0.0798 and 0.0684 respectively, improvements of 145% and 110% over the mechanism-based Proximity, 40% and 20% over the data-driven method node2vec, and 53% and 31% over the data-driven method edge2vec respectively. Furthermore, applying the Rank Aggregation method to integrate the prediction results of both models leads to precision improvements of 75% and 105%, further enhancing the predictive ability of the models. The prediction results on real-world clinical data of the two models demonstrate sound prediction performance, highlighting their potential to promote the effective application of TCM in novel indications.