Quasi-Stellar Object (QSO), the most distant celestial body observed by humans, has important scientific value for the universe evolution.Quasars are far away from the earth, and their redshift values are large, which results in few features appearing in the optical observation window. Hence, constructing a QSO template is a difficult task, and then making the automatic identification of QSO become an urgent problem. The abnormal characteristics extraction and analysis of QSO spectra are helpful to solve the above problems, there by further providing strong evidence for exploring the mysteries of the universe. The outlier detection method, one of the main research contents in the data mining field, can detect rare data objects and anomalous characteristics from massive size data. Therefore, outlier detection can facilitate novel schemes for identifying rare QSOs and achieving validation. As a new generation of big data distributed processing framework, Spark provides an efficient, easy-to-implement and reliable parallel programming platform for analyzing and processing massive celestial spectra. The overarching goal of this paper is to investigate parallel detection methods based on sparse-subspace for QSO anomalous characteristics. We aim to optimize the performance of parallel abnormal detection through the virtue of the high-performance data processing capacity of the Spark programming model on clusters. This research embraces the following three modules, namely, QSO spectral feature reduction, sparse-subspace construction and search of QSO spectral data, and parallel algorithm design and analysis of QSO abnormal characteristics extraction. The QSO spectral feature reduction module exhibits superb performance in speeding up abnormal characteristic’s detection efficiency by the attribute correlation analysis. Specifically, some spectral feature lines with clustering structure are identified, which are usually concentrated in dense regions and are meaningless for detecting anomalous spectral features. The module aims to prune the redundant feature lines so as to narrow the search range of abnormal quasars. The second module is the sparse-subspace construction and search module, which extends the particle swarm optimization method to search sparse subspaces so as to obtain the anomalous features quickly. At the heart of this module is the determination of the sparse-subspace that contains QSO spectra anomalous features, where the subspace density of QSO spectra is measured by a threshold of sparse coefficients. In the third module, a parallel detection algorithm for abnormal spectral data under the MapReduce framework is proposed. The algorithm consists of two MapReduce: parallel data reduction strategy and sparse-subspace parallel search technique. Finally, the detectedanomalous features of some QSOs are analyzed, measured and verified by human eyes, which fully demonstrates that the sparse-subspace can provide effective support and strong evidence for identifying candidate sources of special and unknown QSOs.