There are massive data in big data sets， and when the data scale expands to a certain extent， the processing efficiency of discrete point detection is limited. Therefore， a dynamic outlier detection algorithm based on CART decision tree was proposed. Firstly， the abnormal data standard of large data set was divided， the data dispersion degree by variance was measured， the abnormal data sample association rule matrix by support vector machine was established， the abnormal data range of large data set was clarified， and the amount of outlier detection calculation by dynamic meshing strategy was reduced. Then， the classification and regression trees（CART） decision tree method was used to take Boolean detection at the branch nodes， unify the data to be detected as continuous data， arrange the training data set in ascending order， calculate the maximum information gain of the data， prune the decision tree until no non leaf nodes can be replaced， and obtain the dynamic detection results of outliers. Simulation results show that the proposed algorithm has high outlier detection accuracy， short detection time， significant computational advantages， and can provide positive help for the reliable application of large data sets.