For a stable dynamic system, probability density distribution (PDD) of a system variable is relatively stable, and if there is a change in dynamic structure of a system, the PDD of the system variable will have some change correspondingly. According to this characteristic of PDD of a dynamic system, in this paper we present two new methods, namely, skewness index and kurtosis index, to detect an abrupt change in a time series by means of identifying some small changes in PDD. Tests on model time series indicate that skewness index and kurtosis index can be used to identify an abrupt change, such as abrupt change in parameter of an equation and abrupt dynamic change. Thus, we provide a new approach to detecting abrupt change in time series based on PDD. Further studies show that the detected results of the skewness index and kurtosis index are almost independence of the length of a subseries.