R&D investment is an important way to improve scientific and technological innovation capabilities. In an increasingly competitive market, the rapid changes in science and technology have brought new opportunities for enterprise development. However, if production factors cannot be rationally allocated, low allocation efficiency or low allocation efficiency is likely to occur. The phenomenon of excessive overflow of production factors makes the input factors unreasonable and causes the problem of lowering the economic output of enterprises. Therefore, this article analyzes the feasibility and timeliness of R&D investment from factors of production and enterprise output performance based on data mining. The problem is to optimize the rational allocation of future factors of production and provide assistance in achieving a combination of existing and new factors of production. For this test, we selected the companies listed in the Growth Enterprise Market and the survey period is from 2018 to 2020. The data is taken from the Guotaian database, some of which is obtained by manually reading the company’s annual report, and a multiple regression analysis model is established and tested. The relationship between R&D investment, production factors, and corporate performance is obtained. The group regression method is used to test the impact of production factors on R&D. Whether input and corporate performance have a moderating effect, and the specific moderating and lagging effects of production factors are investigated. Experiments have proved that the nonstandardized coefficient of R&D investment intensity and operating gross profit margin is 0.714, and the value of 9.296 is positive and significant. Each increase of enterprise R&D investment intensity by 1 will increase operating gross profit margin by 0.714. The coefficient of operating gross profit margin is much smaller than the coefficient of Tobin’s value. This shows that the factor of production has a great influence on the relationship between R&D investment and corporate performance. It has the importance of being a specific practical guide for guiding GEM companies in my country with different elemental intensities to carry out R&D activities and improve corporate performance.

中文翻译：

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基于生产要素市场流通的智能数据挖掘

研发投入是提高科技创新能力的重要途径。在竞争日益激烈的市场中，科技的日新月异为企业发展带来了新的机遇。但是，如果生产要素不能合理配置，很可能会出现配置效率低或配置效率低的情况。生产要素过度溢出现象，使投入要素不合理，造成企业经济产出下降的问题。因此，本文基于数据挖掘，从生产要素和企业产出绩效两个方面分析研发投入的可行性和及时性。问题是优化未来生产要素的合理配置，为实现现有生产要素和新生产要素的结合提供帮助。本次测试，我们选取​​了创业板上市公司，调查时间为2018年至2020年。数据来自国泰安数据库，部分数据为人工阅读公司年报，并进行多元回归建立分析模型并进行测试。得到研发投入、生产要素与企业绩效之间的关系。组回归法用于检验生产要素对研发的影响。考察投入与企业绩效是否具有调节作用，考察生产要素的具体调节作用和滞后作用。9.296 的值是积极的和显着的。企业研发投入强度每增加1，营业毛利率将增加0.714。营业毛利率系数远小于托宾值系数。由此可见，生产要素对研发投入与企业绩效的关系有很大影响。具有指导我国不同要素强度的创业板企业开展研发活动、提高企业绩效的具体实践指南的重要意义。