Additivity of multidimensional KPIs (key performance indicators) was used to achieve root cause location for large-scale Internet services. The anomaly caused by one or more root causes usually results in the change of a large number of relevant KPIs. A pruning search model based on anomaly similarity and effectiveness factor for root cause location (PASER) was proposed, which indicated the probability of candidate set becoming root cause using potential score based on the anomaly propagation model of multi- dimensional KPI. The pruning search algorithm used in PASER also managed to reduce the location time to about 5.3 seconds on average. In addition, the selection of time series prediction algorithm was also discussed. PASER had finally achieved a performance of 0.99 F-score on the experimental dataset.