The invention discloses a breaker fault diagnosis method based on linear discriminant analysis and a support vector machine. An online monitoring device collects current of an opening/closing coil of a breaker and uses the current as data samples for fault diagnosis, and the data samples including a data sample during normal operation and data samples during faults; y characteristic quantities are extracted from the m data samples to form a data sample of m rows and y columns, and the data sample is standardized to form an original characteristic matrix R of n rows and y columns; an LDA algorithm is used to carry out mapping transformation on the original characteristic matrix to obtain a transformed characteristic matrix R' of m rows and k columns and a coefficient matrix W of a transformation function; the transformed characteristic matrix R' serves as a training data set of the support vector machine to optimize internal parameters of the support vector machine; and faults of the breaker can be diagnosed intelligently based on an LDA and support vector machine model. The method is high in precision, and provides guarantee for reliability and stability of a power system.