Abstract: Isolation forest (iForest) cannot effectively detect local outliers and the outlier score threshold is difficult to be precise, therefore, an isolation forest outlier detection method based on node evaluation (NE) and maximum between-class variance (Otsu) was proposed. First, the scoring mechanism was introduced into the node depth and relative mass at the same time during the sample assessment process, so that the algorithm was sensitive to global and local outliers. Afterwards, to accurately set the score threshold, the Otsu method was used to adaptively determine the outlier score threshold. Finally, the effectiveness of the proposed method was verified on different datasets. Results show that the proposed method can effectively balance the detection of global and local outliers, and can improve the accuracy of detection of outliers in isolation forests.