MOTIVATION: Predicting microbe-drug associations (MDgAs) is critical for understanding the role of microbes in drug metabolism, exploring their interactions with host physiology, and advancing personalized therapy. However, traditional methods face challenges in dealing with data sparsity, information imbalance, and the extraction of complex biological knowledge, which limit the accurate prediction of microbe-drug associations. Therefore, developing a computational model that can efficiently integrate multi-source data and address the challenges of data sparsity and information imbalance is essential. RESULTS: The paper proposes a model that integrates knowledge graphs and contrastive learning. By constructing both local and non-local association graphs, the model effectively captures the complex relationships between microbes and drugs. We preprocess and model the embedding representations of microbes and drugs, and design a multi-level interactive contrastive learning mechanism to optimize the information flow both within and outside the graph. Experimental results show that our model significantly outperforms existing methods in metrics such as AUC and AUPR, providing an efficient solution for predicting microbe-drug associations. AVAILABILITY AND IMPLEMENTATION: The source code is available at: https://github.com/SJshujuan/KGCLMDA. The code used in this study is also available on Zenodo: https://doi.org/10.5281/zenodo.16754402.