In view of the current problems that facial expressions are difficult to recognize under conditions of lighting changes and occlusion， as well as the low recognition rate of pessimistic emotions， this paper proposes a facial expression recognition algorithm based on graph convolutional cascade classification based on improved dense connection network and fusion of facial key point features. Since different deep learning models have their own advantages in facial expression recognition， dense connection network has a high accuracy rate in recognizing optimistic and calm expressions， but has a weak recognition effect on pessimistic expressions. Therefore， this paper first uses wavelet transform， key part mask attention mechanism and binary tree classifier to improve the dense connection network， and constructs the I-Densenet （Improved-DenseNet） module for the rough division of optimistic， calm and pessimistic facial expressions to improve the recognition rate of rough division； Secondly， the graph convolutional neural network based on the fusion of facial key point features is used to fine-grainedly divide the pessimistic expression of the face to improve the recognition rate of pessimistic expression. Finally， this paper constructs the D-GFK network （DenseNet-GCN and face key point network） by cascading the improved dense connection network with the graph convolutional neural network based on key point feature fusion， combining the advantages of different models to comprehensively improve the accuracy of facial expression recognition. Experiments show that the model proposed in this paper has achieved good recognition results in facial expression recognition tasks.