With the rapid development of machine learning, acquiring high-performance classification models with minimal annotation costs has become an urgent issue. This study aims to address this challenge through selective sampling using active learning. However, traditional optimization experimental design algorithms have failed to fully utilize sample label information. Therefore, a novel active learning algorithm combining neighborhood density and uncertainty is proposed to improve the direct experimental design by selecting representative and uncertain samples. Experimental results demonstrate that compared to traditional methods, this algorithm exhibits significantly superior performance on four standard image datasets.