Addressing the limitations of LOF anomaly detection algorithm, such as with high time and space complexity and insensitivity to cross anomalies and outliers around low-density clusters, this paper proposes isolation-based data extracting LOF (iDELOF) anomaly detection algorithm, which puts the isolation-based K-nearest-neighbor search space extraction (iKSSE) in front of LOF, to efficiently cut out a large amount of useless and interfering data and obtain a more accurate search space. Based on this, the theoretical and four groups of experimental analysis are completed, and in each group of experiments, iDELOF is compared with many typical algorithms such as LOF, iForest and iNNE. The results show that iDELOF improves the detection capabilities of LOF by widening the gap between the local outlier factor of normal and abnormal points, and enhancing the ability to identify cross anomalies and abnormal points around low-density clusters.Additionally, iDELOF has the same obvious superiority as LOF in identifying axis-parallel anomalies. The data subset obtained by iDELOF through iKSSE is significantly smaller than the original dataset and the data volume of most subsets is less than 1% of the original dataset. Therefore, the time and space complexity of iDELOF is significantly reduced, and the larger the amount of data in the original dataset, the more obvious the superiority is. When the amount of data is large enough, the running time of iDELOF will be lower than that of the IF algorithm.