Abstract: In the domain of multi-behavior sequence recommendation, Graph Neural Networks (GNNs) have been widespreadly adopted, yet they have limitations, notably in terms of adequately modeling the collaborative signals that exist between different sequences and addressing the challenges posed by long-distance dependencies. To bridge these gaps, a novel framework named GraphMLP-Mixer has been introduced. This innovative framework begins by constructing a global item graph, which is designed to bolster the model’s capacity to encapsulate the collaborative signals that are present across sequences. It then merges the perceptron-mixer architecture with graph neural networks, resulting in a graph-perceptron mixer model capable of delving deep into the intricacies of user interests. GraphMLP-Mixer stands out for its two principal strengths: It not only succeeds in effectively capturing the global dependencies inherent in user behaviors but also manages to alleviate the issue of excessive information compression. Furthermore, the framework boasts remarkable improvements in terms of time and space efficiency, with its complexity scaling in a linear fashion with the number of user interactions, thus outperforming existing GNN-based models in the realm of multi-behavior sequence recommendation. The robustness and efficiency of GraphMLP-Mixer in tackling the complexities of multi-behavior sequence recommendation have been thoroughly validated through extensive experimentation on three diverse and publicly available datasets.