Due to the challenge of high dimensionality， insufficient utilization of spatial-spectral information and limited local structure property expression in hyperspectral images， a hyperspectral anomaly detection algorithm based on 3D convolutional autoencoder and low rank representation is proposed in this paper. Firstly， the spectral-spatial features of hyperspectral images are extracted by 3D convolutional autoencoder. In order to precisely represent the local similarity， a new loss function is proposed to constrain the central pixel and it's surrounding pixels to extract more discriminative features. And then， the Density Based Spatial Clustering of Applications with Noise （DBSCAN） algorithm is used to construct the background dictionary， and the abnormal region is separated by low rank representation on the feature map. Finally， the detection result is obtained by fusing the reconstruction error obtained by 3D convolution autoencoder and abnormal region detection result. We carry out objective and subjective anomaly detection experiments on two real hyperspectral datasets. The results demonstrate that the proposed algorithm detect abnormal targets more accurately compared with other algorithms.