In order to solve the problems of multi-entity and multi-matching categories and complex context information in the field of discourse level relation analysis and recognition, this paper proposes an English discourse structure analysis and recognition method considering multifeatures by fusing entity feature information and context feature information. Firstly, a structural analysis and recognition framework considering multiple features is proposed. Then, the mechanisms of entity recognition unit and context relation recognition unit are introduced in detail. Finally, comparative experiments and ablation experiments are carried out through public datasets and self-selected datasets to verify and analyze the superiority of the proposed model, and the recognition efficiency of the model and the influence of related parameters are experimented and analyzed. On the two datasets, the proposed method can maintain the optimal performance in both indicators F1 andIgnF1. Compared with other suboptimal recognition models, the proposed method improves 1.65% and 1.13% respectively on F1, 2.78% and 1.58% respectively onIgnF1. Experiments show that the proposed model can extract the key feature information representing the discourse relation, help to understand the context and the relationship between each part of the text, and grasp the overall structure of the text.