Constructing the transition probability matrix for outlier detection by using traditional graph-based methods requires the use of the overall distribution of the data， and the local information of the data is easily ignored， resulting in the problem of low detection accuracy， and using the local information of the data may lead to “suspended link” problem. Aiming at these problems， an Outlier Detection algorithm based on Hologram Stationary Distribution Factor （HSDFOD） was proposed. Firstly， a local information graph was constructed by adaptively obtaining the set of neighbors of each data point through the similarity matrix. Then， a global information graph was constructed by the minimum spanning tree. Finally， the local information graph and the global information graph were integrated into a hologram to construct a transition probability matrix for Markov random walk， and the outliers were detected through the generated stationary distribution. On the synthetic datasets A1 to A4， HDFSOD has higher precision than SOD （Outlier Detection in axis-parallel Subspaces of high dimensional data）， SUOD （accelerating large-Scale Unsupervised heterogeneous Outlier Detection）， IForest （Isolation Forest） and HBOS （Histogram-Based Outlier Score）； and AUC （Area Under Curve） also better than the four comparison algorithms generally. On the real datasets， the precision of HSDFOD is higher than 80%， and the AUC of HSDFOD is higher than those of SOD， SUOD， IForest and HBOS. It can be seen that the proposed algorithm has a good application prospect in outlier detection.