In order to find out the fault of the pumping unit, reduce production cost and raise production efficiency, it is necessary to judge the working condition of the pumping unit in time and accurately by analyzing the different shape’s indicator diagrams of pumping units. The traditional artificial recognition method can’t realize the realtime diagnosis of the pumping unit working condition. The traditional intelligent algorithm has low recognition accuracy. Therefore, a method based on stacked sparse autoencoder(SSAE) for indicator diagram identification was proposed for fault diagnosis of pumping unit. In this method, the deep and separable features of indicator diagram data were automatically extracted by stack sparse selfencoder,then the learning features combined with the corresponding sample labels were used to carry out supervised training and classification through support vector machine.The measured indicator diagrams of Zhongyuan Oilfield were used to experiment with this method. The experimental results show that the method has high recognition speed and accuracy. The proposed method can help to diagnose the faults of pumping unit quickly and accurately.