The traditional neural network model cannot reflect the macro discourse structure information between different sections in scientific paper, which leads to the incomplete structure and incoherent content of the generated scientific paper summarization. In order to solve the problem, a scientific paper summarization model using macro discourse structure was proposed. Firstly, a hierarchical encoder based on macro discourse structure was built. Graph convolution neural network was used to encode the macro discourse structure information between sections, so as to construct the hierarchical semantic representation of sections. Then, an information fusion module was proposed to effectively fuse macro discourse structure information and word-level information, so as to assist the decoder to generate the summarization. Finally, the attention mechanism optimization unit was used to update and optimize the context vector. Experimental results show that the proposed model is 3.53, 1.15 and 4.29 percetage points higher than the baseline model in ROUGE (Recall-Oriented Understudy for Gisting Evaluation)-1, ROUGE-2 and ROUGE-L respectively. Through the analysis and comparison of the generated summarization content, it can be further proved that the proposed model can effectively improve the quality of the generated summarization.