Due to the high complexity， weak individual differences， and short inter-class distances of fundus image features， pure Convolutional Neural Networks （CNNs） and attention based networks cannot achieve satisfactory accuracy in fundus disease image classification tasks. To this end， Attention Bilinear Involution Neural Network （ABINN） model was implemented for fundus disease image classification by using the involution operator. The parameter amount of ABINN model was only 11% of that of the traditional Bilinear Convolutional Neural Network （BCNN） model. In ABINN model， the underlying semantic information and spatial structure information of the fundus image were extracted and the second-order features of them were fused. It is an effective parallel connection between CNN and attention method. In addition， two instantiation methods for attention calculation based on involution operator， Attention Subnetwork based on PaTch （AST） and Attention Subnetwork based on PiXel （ASX）， were proposed. These two methods were able to calculate attention within the CNN basic structure， thereby enabling bilinear sub-networks to be trained and fused in the same architecture. Experimental results on public fundus image dataset OIA-ODIR show that ABINN model has the accuracy of 85%， which is 15.8 percentage points higher than that of the common BCNN model and 0.9 percentage points higher than that of TransEye （Transformer Eye） model.