Objective Existing recommendation methods based on graph neural networks GNN often overlook the time resident information of valuable users on items within sessions fail to address the impact of unconscious clicks by users and neglect the expressive ability of hidden factors in GNNs. To overcome these limitations this paper proposed a graph neural network session-based recommendation model based on the fusion of time resident information TRGNN . Methods This method first processed the time resident information of users on various items and extracted time features using a temporal graph neural network. Subsequently it applied a multi-head attention mechanism to enhance the expressive ability of factors and better extract item features. TRGNN fused time features with item features to obtain final features captured global and local contexts through an attention network and ultimately obtained final recommendations through a prediction layer. Results Comparative experiments were conducted using two real datasets Diginetica and Yoochoose. The experimental results indicated that compared with the optimal baseline model the proposed model achieved improvements of 1. 57% and 3. 30% in the MRR@ 20 evaluation indicators and improvements of 1. 10% and 0. 66% in the Recall @ 20 indicators respectively. Conclusion The proposed model demonstrates superior recommendation performance effectively mining latent information and fully utilizing temporal features and item latent features to enhance recommendation accuracy while reducing the impact of unintended user clicks on recommendation precision.