A computer-aided diagnosis method for posterior capsular opacification using multimodal machine learning was proposed for the long incidence cycle and wide screening range of the complication of posterior capsular opacification. The region of interest (ROI) was extracted and the white reflective region was filled on the retro-illumination image. Retro-illumination image and visual quality data can be input into constructed heterogeneous low-rank multimodal fusion network (HLMF) simultaneously for performing feature extraction and fusion, and multimodal information was fused based on channel product. Overfitting problem was solved by low-rank decomposition of convolution kernel parameters, and the Focal Loss was chosen to solve the problem of uneven category. The pre-training and corrupted augmentation methods were used in the training process to better extract and fuse the features of single modality. The accuracy and F1 score of 10-fold cross-validation of the algorithm on the posterior capsule opacification dataset were 95.63% and 96.72%, respectively. Experimental results show that the proposed algorithm can extract single modality features and perform feature fusion well, and has better performance compared with other multimodal fusion models.