The collaborative filtering recommendation algorithm framework based on graph convolutional neural network is the most advanced recommendation algorithm framework at present. The framework does not pay attention to the timing of interaction occurrence in the feature learning of user-item interaction embedding vector, but in actual situations, users- Item interaction generally has obvious timing characteristics and is an important factor affecting recommendation performance. Based on this, a graph convolution collaborative filtering recommendation algorithm based on time series features is proposed, which redo multiple data sets, retain the original information of the data sets, especially the time series features, and summarize the historical time series information of user-item interaction in the data set. It is parameterized and put as an important feature input to the high-order cooperative signal transmission of graph convolutional network model training. Set up multiple sets of ablative experiments on three publicly available official datasets-Gowalla, Yelp and Amazon-book, and use recognized evaluation indicators-ndcg and recall to evaluate the performance of the recommendation algorithm. The experimental results show that under the same parameter Settings, the figure convolution collaborative filtering recommendation algorithm based on temporal characteristics performance beyond the existing same type figure convolution, collaborative filtering recommendation algorithm to verify the timing characteristics are recommended to improve effect of the positive role, improve the efficiency of model training and prediction, shooting more efficiently solve the problem of network information overload, to satisfy the higher application requirements.