In real-world scenarios, a large amount of noise in user historical behaviors obstructs the reflection of their genuine interests. The long-tail distribution of user-item interactions also makes it difficult to capture interest evolution patterns from historical sequences. Moreover, as user behavior sequences continue to grow, solely relying on conventional sequence models is insufficient to extract user interest information and learn accurate sequence representations, thus limiting recommendation accuracy. To address these issues, we propose a self-supervised graph neural sequential recommendation model called LS4SRec, which disentangles users’ long- and short-term interests. Specifically, LS4SRec constructs two independent interest encoders to extract users’ long- and short-term interests. By utilizing the global user behavior sequence graph WITG to provide additional collaborative signals for each interaction sequence, we alleviate the issue of data sparsity. Subsequently, contrastive learning is applied to WITG to remove noise information and enhance the sequence representation. Further, interest allocation matrices and sequence models are utilized to model users’ interest evolution patterns. Finally, we introduce sequence graph data augmentation methods and long- and short-term interest pseudo-label construction methods to generate unsupervised signals that assist in model training. Extensive experiments conducted on real-world data validate the effectiveness of our proposed model. Our model implementation codes are available at the link https://github.com/jiubaoyibao/LS4SRec .