Anomaly detection in hyperspectral images (HSIs) faces various levels of difficulty due to the high dimensionality, redundant information and deteriorated bands. To address these problems, we propose a novel unsupervised feature representation approach by incorporating a spectral constraint strategy into adversarial autoencoders (AAE) without any prior knowledge in this paper. Our approach, called SC_AAE (spectral constraint AAE), is based on the characteristics of HSIs to obtain better discrimination represented by hidden nodes. ⋯ Considering the different contribution rates of each hidden node to anomaly detection, we individually fuse the hidden nodes by an adaptive weighting method. A bi-layer architecture is then designed to suppress the variational background (BKG) while preserving features of anomalies. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods.