The invention discloses an ANFIS-based electric vehicle permanent magnet synchronous motor fault classification method. The method comprises the following steps: faults are classified, and a training sample set is built through acquiring various fault data; and an adaptive neural fuzzy inference system is built, winding current in the fault data for various fault types in the electric vehicle permanent magnet synchronous motor serves as an input, one output is given for each fault type, a membership function for the input and the output is selected, system training target errors are set, a hybrid learning algorithm is used for training parameters of the membership function, and thus, input membership function parameters and output membership function parameters in the adaptive neural fuzzy inference system are thus determined. Through diagnosing the faults of the permanent magnet synchronous motor, experimental data are obtained, the experimental data are inputted to the adaptive neural fuzzy inference system, a diagnosis result is obtained, and a fault type is determined according to the diagnosis result, and thus, fault classification is completed. Strong-operability, high-efficiency, economic and high-accuracy diagnosis is realized.