To solve the problem that the two-tower neural network in the recommendation system is difficult to learn the interaction information between the user side and the item side and the graph connection information， a new algorithm TSN （Two-stage Siamese graph convolutional Neural network recommendation algorithm） was proposed. First， a heterogeneous graph based on user behavior was built. Then， a graph convolutional Siamese network was designed between the two-tower neural networks， so as to achieve information interaction while learning the connection information of the heterogeneous graph. Finally， by designing a special structure of two-stage information sharing mechanism， the neural networks on the user side and the item side could transmit information dynamically and bidirectionally during the training process， and neural network cascading was effectively avoided. In comparative experiments on MovieLens and Douban movie datasets， the NDCG@10， NDCG@50， NDCG@100 of the proposed algorithm are 11.39% to 23.98% higher than those of the optimal benchmark algorithm DAT （Dual Augmented Two-tower model for online large-scale recommendation）. The results show that the proposed algorithm can alleviate the problem of lack of information interaction in the two-tower neural network； and significantly improves the recommendation performance compared with the previous algorithms.