Although the current generative AI academic writing assistant has deeply penetrated the literature retrieval and data analysis of business academic research, whether it can effectively improve the efficiency of business academic research remains to be verified. In this paper, the generative AI academic writing assistant based on business academic research is divided into two modules, information extraction and writing output, to form a business academic writing assistant model. The model proposes a Bert-based extractive summarization method in the extraction of key information of academic text, adopts BertSum to extract the feature vectors of academic text, uses BiGRU to capture the contextual relationship between sentences, integrates GRTU encoder and attention mechanism to accurately extract the relevant information, and utilizes the classification layer to judge whether the sentence stays or goes. In terms of academic text writing output, a selector is utilized to filter out important academic text arguments, and a rewriter is used to generate the corresponding complete academic research content. Logistic regression model was chosen as the research analysis tool, research samples were selected, business academic research efficiency was set as the dependent variable, and the prediction model was constructed based on the results of regression analysis parameter estimation of the seven independent variables. The prediction model of business academic research efficiency predicted 236 students with more than 80.00% accuracy for all three academic research efficiencies.
