Abstract: Bayesian network(BN) is an effective framework for the representation and inference of uncertain knowledge.Qualitative probabilistic network(QPN) is an Abstraction of general BNs.By means of QPN,the representation of uncertainties is simplified and the efficiency of inference is improved.In recent years,QPN has become an important research issue in uncertain artificial intelligence and knowledge discovery.In this paper,we analyze and summarize the currently principal research findings of QPN,including its representation,inference and applications.As for the representation,we summarize qualitative influence,qualitative additive synergy,qualitative product synergy and intercausal influence.As well,the problems and representative solutions for QPN representation are discussed.As for the inference,we introduce the inference algorithm and discuss the problem of inference conflicts.We then mainly analyze and compare the representative methods for resolving conflicts.As for the application,we summarize the QPN-based ideas for knowledge discovery and decision support.In addition,based on the analysis and summary of existing research findings,we also point out some trends and emphasis of further study of QPN-related issues.