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The outlier detection problem has become the focus of research in the field of data mining. At present, there are many kinds of mature outlier detection methods. But when the data has high dimensional mixed property, or there are assembled outliers, the result of these methods become unsatisfactory or imapplicable. Therefore, in view of the shortcomings of these existing methods, a new outlier detection method is proposed in this paper. Meanwhile, we designed the outlier detection platform in time and space domain. For time and spatial series datasets, the platform is based on cross-correlation analysis and self-organizing maps (SOM) neural network clustering. It can be proved by experiments that the platform has higher detection rate and reliability. By the way, the platform is built in a modular and hierarchical way with good openness and extensibility.