Service running data reflects the service running state in the cloud environment. If there are service running data anomalies in the cloud environment, the operation of related software and the use of users will be affected. Traditional software anomaly detection methods usually neglect the information quantity provided by different dimension attributes of running data and the context environment of software running. Thus, the anomaly detection is inaccurate. Therefore, A service running data anomaly detection method based on weighted LOF and context judgment in cloud environment is proposed. Firstly, the dimension attributes of running data are weighted by the information entropy method, and the running data are judged by weighted LOF algorithm for the first anomaly detection. Secondly, the context information of service at runtime is considered comprehensively. The correspon- ding results are obtained after the second anomaly detection. Experiments show that this method can effectively detect service running data anomalies in the cloud environment.