To solve the fault diagnosis of oil-filled transformer, an approach based on attribute reduction of differential rough set with directed acyclic graph-support vector machine (DAG-SVM) is proposed to rapidly identify fault reasons. Fault decision table of volume of dissolved gas in oil-filled transformer is firstly established according to historical data and corresponding fault type. Then data of condition attributes are discretized by means of equal frequency division method, differentiated attribute reduction is conducted by means of discernibility matrix in rough set theory for decision table, and diagnosis rules between every two kinds of faults are set up, so the redundant attributes of low identification are removed. Finally, multi-classified diagnosis classifier DAG-SVM is constructed by SVMs, in which the data of reduced attributes are character vectors. Case analysis indicates that this method improves the accuracy of fault diagnosis in system detection.