In recent years, recommender systems based on graph neural network (GNN) have made good use of the interaction structure of interaction data to learn user and item representations. However, existing recommendation models based on GNN often ignore the temporal information of interactions during aggregation, which makes it difficult to model the change characteristics of users’ interests. As a result, this causes overfitting of the recommendation model to data, and a lack of diversity in the recommendation results, thereby making it difficult to satisfy the more diversified needs of users. To this end, a temporal information-enhanced diversified recommendation model is proposed. First, an attention mechanism is employed to capture and fuse temporal information and interaction information from historical user-item interactions. Meanwhile, a feature disentanglement module is designed to disentangle smoothed global features from salient, highly discriminative key signals to reduce feature redundancy and improve representational clarity. Subsequently, neighbour selection is adopted to highlight inter-node differences and conduct graph convolution, with a layer attention mechanism employed to alleviate over smoothing. Finally, the learning of items in the long-tail category is enhanced by reweighting loss to improve the diversity.