The invention relates to a non-linear fault identifying method based on a PCA (principal component analysis) contribution diagram, and includes five steps, namely, collecting data, extracting the base of the feature space, extracting the non-linear principal component, detecting faults and identifying faults. The invention puts forward a new method for extracting the base of the feature space, reduces the linear redundancy after the data are mapped to the feature space by extracting the base and decreases the calculation amount of KPCA when training samples are large in quantity. The invention adopts the contribution diagram to identify faults, calculates out the contribution diagram and the control limit for each variant in the process and determines the responsibility for the occurrenceof faults that each variant should shoulder when each variant is out of control through the relationship between the contribution diagram and the control limit. The identifying method overcomes the trouble in identifying faults due to the fact that the input space and the feature space cannot be converted freely.