To improve outlier detection in high-dimensional data stream, a novel high-dimensional data stream outlier detection (DSOD) algorithm based on angle distribution was proposed. To identify the normal point, border point and outlier accurately, the method of angle distribution-based outlier detection algorithm was employed. To reduce the computational complexity, a smallscale calculation set of data stream was established, which is composed of normal set, border set. To solve the problem of concept drift, an updated mechanism for the normal set and border set was developed. The experimental results on real data sets demonstrate that DSOD is more efficient than Simple variance of angles (Simple VOA) and angel-based outlier detection (ABOD) and is very suitable for the outlier detection of large data streams.