Drug repositioning aims to discover new therapeutic pathways for approved drugs, thereby reducing drug development costs. However, in computational drug repositioning, validated drug-disease associations are sparse, and negative samples can only be randomly selected from unvalidated associations that inherently conflate truly non-interacting pairs with undetected therapeutic relationships. This fundamental challenge particularly affects deep learning models, which require high-quality training samples to achieve optimal performance. To transcend this basic limitation, we propose a novel sample selection strategy tailored explicitly for deep learning models to filter reliable negative samples and potential positive samples. First, grounded in the low-rank structure of the drug-disease association matrix, we employ matrix completion algorithms to infer unvalidated association information. Subsequently, we develop an unsupervised clustering algorithm that comprehensively considers the completed probability scores and ranking positions of candidate diseases for each drug, categorizing all samples into distinct categories based on their confidence levels. Finally, we select reliable negative samples and potential positive samples from the clustering results to train deep neural networks for drug repositioning. Substantial experimental results verify that our proposed sample selection strategy enhances the performance of deep learning-based computational drug repositioning methods.