Predicting in advance whether pedestrians on both sides of the road have the intention to cross the street or whether crossing behavior will occur after a period of time is one of the important challenges facing self-driving cars. How to effectively fuse the multi-information from these different modalities is an important issue in accurately predicting pedestrian crossing intentions. Therefore, this paper proposes a multi-information fusion prediction model based on a hybrid attention mechanism. The model uses an image feature fusion network based on a cross-attention mechanism to extract complementary information between the original image and the semantic image and to make the model more attentive to the parts of the image that are relevant to the behavior of the pedestrian crossing the street. We also propose a hierarchical gated recurrent unit(GRU) module incorporating an attentional mechanism to capture the effects of different modalities of non-visual information on pedestrian crossing intentions. Finally, the proposed model is compared on the PIE and JAAD datasets and achieves leading performance, and extensive ablation experiments are conducted on the proposed module to prove its effectiveness.