The invention discloses a local outlier detection method based on density clustering. The method includes the steps: (a) acquiring data cluster number and clustering center of a to-be-detected dataset; (b) calculating the mean and standard deviation of description features of each data object in different data clusters; (c) detecting according to 3sigma criterion to obtain outliers of each data cluster. By adoption of the local outlier detection method based on density clustering, easiness in setting of parameters is realized, the method is applicable to datasets with different density regions and arbitrary shapes and can be used for detection of local outliers, accuracy of outlier detection results is high, algorithms are insensitive to selection of parameter values, and excellent robustness is achieved.