Question generation refers to the automatic generation of questions by computer systems based on given paragraphs and answers, which is one of the research hotspots in natural language processing. Although previous work has made great progress, there are still some limitations: (1) The rich structural information hidden in word sequences is ignored. (2) Current studies focus on sequence-to-sequence-based neural networks to maximize the use of question-and-answer information in the context. However, the context often contains a large number of redundant and irrelevant sentences, and these models fail to filter redundant information or focus on key sentences. To address these limitations, we use a Graph Convolutional Network (GCN) and a Bidirectional Long Short Term Memory (Bi-LSTM) Network to capture the structure and sequence information of the context simultaneously. Then, we use a contrastive learning strategy for content selection to fuse the document-level and graph-level representations. We also use a dual attention mechanism for the passage and answer. Next, we use the gating mechanism to dynamically assign weights and merge them into context information to support the question decoding by modeling their interaction. We also conduct qualitative and quantitative evaluations on the HotpotQA deep question-centric dataset, and the experimental results show that the proposed model is effective.