For the problem of over-smoothing in the existing recommendation algorithms based on Graph Neural Network （GNN）， a collaborative filtering recommendation algorithm based on deep GCN was proposed， namely Deep NGCF （Deep Neural Graph Collaborative Filtering）. In the algorithm， the initial residual connection and identity mapping were introduced into GNN， which avoided GNN from falling into over-smoothing after multiple graph convolution operations. Firstly， the initial embeddings of users and items were obtained through their interaction history. Next， in aggregation and propagation layer， collaborative signals of users and items in different stages were obtained with the use of initial residual connection and identity mapping. Finally， score prediction was performed according to the linear representation of all collaborative signals. In addition， to further improve the flexibility and recommendation performance of the model， the weights were set in the initial residual connection and identity mapping for adjustment. In order to verify the feasibility and effectiveness of Deep NGCF algorithm， experiments were conducted on datasets Gowalla， Yelp-2018 and Amazon-book. The results show that compared with the existing GNN recommendation algorithm such as Graph Convolutional Matrix Completion （GCMC） and Neural Graph Collaborate Filtering （NGCF）， Deep NGCF algorithm achieves the best results on recall and Normalized Discounted Cumulative Gain （NDCG）， thereby verifying the effectiveness of the proposed algorithm.