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It is an important means to ensure safe and efficient production of oil wells to diagnose pumping wells by means of indicator diagram. In view of the problem that the feature extraction of the present indicator diagram only uses the amplitude spectrum of its Discrete Fourier Transform (DFT) and ignores its phase spectrum, which leads to the low recognition rate, presents a method to identify the amplitude spectrumand phase spectrum of the fused DFT. Firstly, the DFT of the indicator diagram sequence is calculated and linearly independent and orthogonal eigenvectors are constructed according to its DFT amplitude spectrum and phase spectrum. Secondly, The characteristic vectors of the indicator diagram of the known fault-type are trained, and the Multi-class Support Vector Machines classification model Multi-class Support Vector Machinesis constructed. Finally, the LIBSVM identification method is used to classify the unknown type of indicator diagram to diagnose the working condition of the pumping well. The measured results show that this method can effectively improve the identification rate of the indicator diagram and provide technical support for accurate analysis, diagnosis and real-time optimization control of the oil well condition, compared with the method of using only the amplitude spectrum of DFT.