Modulation format identification (MFI) and optical performance monitoring are important for elastic optical networks. We propose a cascaded module-based MFI and optical signal-to-noise ratio (OSNR) estimation method. Polar coordinates were used as features to distinguish the modulation formats (MFs) and estimate the OSNR. Dilated convolution was adopted to increase the receptive field and reduce computational complexity. Four commonly used MFs were investigated: dual-polarization (DP) quadrature phase shift keying, 8QAM, 16QAM, and 32QAM. A 32G baud DP transmission system is established, and the results are discussed. The results show that MFI accuracy can reach 100% when the OSNR is less than the 7% forward error correction limit, and the accuracy of OSNR monitoring is close to 100%. Two factors, sample length and nonlinearity, were further investigated. The results show that the MFI and OSNR estimators can achieve perfect performance when the sample length is 3000. Simultaneously, the accuracy of the MFI remained at 100%, and the accuracy of the OSNR estimator decreased by 1% to 2% when the launch power changed from 0 to 5 dBm. Furthermore, two modules with the same structure, DC-CNN and CNN, were compared. The results show that the two models can achieve similar accuracies and that the DC-CNN has the least number of parameters. Finally, experimental verification was conducted to ensure the practical applicability of the proposed method.