Open clusters (OCs) are important objects in exploring the structure and history of the Milky Way. Large amount of sky survey data can be used to detect OCs. However, analyzing these data manually has become a bottleneck of OC identification. This study proposes a multi-scale density feature learning (MSDFL), which includes the open cluster kernel density map to visualize the features of OCs; and open cluster identifying network, which is a deep learning model used to perform identifying with the maps. A test set and experimental region are utilized to evaluate the effectiveness of our method. For OCs that stand out as significant overdensities, experimental results show that the MSDFL method can achieve the accuracy of 94%. Lastly, the proposed method can successfully identify real OCs in the experimental sky region. The code is available at: https://gitee.com/colab_worker/cluster_search.

开放星团（OC）是探索银河系的结构和历史的重要对象。大量的天空调查数据可用于检测OC。但是，手动分析这些数据已成为OC识别的瓶颈。这项研究提出了一种多尺度密度特征学习（MSDFL），其中包括开放集群核密度图以可视化OC的特征。开放集群识别网络，这是一种用于对地图进行识别的深度学习模型。利用测试集和实验区域来评估我们方法的有效性。对于显着超重的OC，实验结果表明，MSDFL方法可以达到94％的精度。最后，该方法可以成功地识别实验天空区域中的真实OC。该代码位于：