A novel modulation format recognition (MFR) scheme based on multiple Stokes sectional planes images by generative adversarial network (GAN) is proposed and demonstrated to adapt to next-generation elastic optical network (EON). The application of the encoder, along with the suitable loss function, is able to achieve better performance with regards to MFR of GAN. Experimental verifications were performed for the polarization division multiplexing (PDM)-EON system at a symbol rate of 12.5GBaud. Five modulation formats, including PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-8QAM, PDM-16QAM, were recognized by our scheme under the condition of practical optical signal-to-noise ratio (OSNR) over both back-to-back transmission and 25km standard signal-mode fiber (SSMF). Specifically, the minimum required OSNR of PDM-16QAM signal to achieve 100% MFR success rate is 18 dB, which is lower than its corresponding 7% forward error correction (FEC) threshold. Results show that, compared with three other machine learning algorithms, the proposed scheme obtains the higher recognition accuracy in the case of the same OSNR. Moreover, the training data required by the proposed scheme is less than the traditional convolutional neural network (CNN) in MFR task, which means the training cost of the neural network is greatly reduced by using GAN.