[目的/意义] 数字图书馆图像资源检索服务需要准确和快速的响应。针对现有深度学习图像检索方法存在特征不够精细和检索速度慢等问题，提出了多组双层稀疏自动编码器（Multi-Group-Two-Layer Sparse Auto-Encoder，MGTL-SAE）精细特征图像资源快速检索方法。[方法/过程]首先，GoogLeNet预处理模型提取图像资源深度学习特征作为初始特征，并用密度峰值聚类算法划分初始特征集。其次，依据聚类类别数构建MGTL-SAE网络结构，聚类划分图像子集中随机抽取图像块初始化MGTL-SAE的均值和方差参数，梯度下降法估计MGTLSAE网络参数以学习特征直到目标函数收敛，迭代密度峰值聚类和MGTL-SAE参数估计以计算精细特征直到特征基收敛。最后，在图像资源精细特征密度峰值聚类划分子集上快速检索查询样本图像。［结果/结论］实验结果表明，MGTL-SAE方法相较于CaffeNet和GoogLeNet等深度学习检索方法具有更高查准率、查全率和平均mAP，更快检索速度，为数字图书馆图像资源检索研究提供了新视角。

［Purpose /significance］Image resource retrieval for digital library needs an accurate and fast response. However, traditional image retrieval methods based on deep learning have the problems of long time for training model, unrefined feature and slow retrieval speed. We proposed Multi-Group-Two-Layer Sparse Auto-Encoder (MGTLSAE) to learn refine features for fast image resource retrieval.［Method /process］Firstly, extracted deep learning features of the image resources with the pre-train model GoogLeNet as initial features which were clustered with the density peak(DP) clustering algorithm. Secondly, designed the network of MGTL-SAE using the number of classes of clusters, initialized the mean and variance parameters of MGTL-SAE with the image blocks selecting from the images according to the image resource sub-sets partitioned by the clusters, estimate the parameters of MGTL-SAE to learn features by the gradient descent algorithm until the cost function is converged, iterate the DP clustering algorithm and estimating the parameters of MGTL-SAE to compute the refined feature until the feature bases are converged. Lastly, retrieved the query sample image fast within the sub-set of image resources partitioned by the DP clusters of refined features.[Result /conclusion] Experiment results show that our method has higher precision, recall and mAP(mean Average Precision) and less retrieval time than the methods based on CaffeNet and GoogLeNet . It also provides a new perspective for the research of image resource retrieval in the digital library.