科技期刊初审效率提升策略--基于投稿日周内效应的研究

Explore the ways of artificial intelligence to identify and utilize the big data of journals, adjust the way of journal manuscript review and improve the efficiency of manuscript initial evaluation according to the collaborative work management mode of artificial intelligence editing and traditional editing mode. Through the study of the author\'s submission behavior to academic journals, this paper finds out whether there is some kind of non-mandatory law in the submission behavior and the relationship between the quality of the articles, so as to provide reference for related theoretical research and academic journals. The paper submitted to the submission system of Journal of Sichuan Forestry Science and Technology from November 28, 2017 to November 28, 2019 (i.e. 2 years, 731d) was analyze. Statistical tests such as chi-square are provided to ensure the validity of the results. Thermodynamic relations are proposed in the context of entropy, and entropy distance is used to measure the disorder of the submission process. The number of offers, the rate of offers, the number of rejected offers, the rate of rejected offers and the rate of rejected offers were compared. L value is used to check and sort the submission day. This paper puts forward a new paper review strategy which focuses on the submission day within a week, which effectively improves the efficiency of the initial evaluation.As a new data utilization model of deep learning with big data by artificial intelligence. This strategy provides a new way for editors to quickly screen articles in the era of big data and artificial intelligence, which will inject more vitality into the publishing process and make it easier to find high-quality articles.