The effect of digital transformation on enterprise technological innovation is reflected in quantity and quality, which may show heterogeneity. In this regard, this paper uses the data of China’s A-share agricultural listed companies from 2015 to 2020 to compare the differential impact of enterprise digital transformation from the perspective of quantity and quality of technological innovation. Firstly, the Tobit model is used to test whether there are differences in the impact of digital transformation on the quantity and quality of technological innovation of agricultural enterprises, and heterogeneity is tested according to the nature of enterprises. Secondly, this paper explores the reasons digital transformation has different effects on the quantity and quality of technological innovation through mechanism analysis. Finally, according to the threshold model, the conditions for digital transformation to promote the quantity and quality of technological innovation of agricultural enterprises are discussed. The empirical results show that, first, the digital transformation of agricultural enterprises only promotes the number of technological innovations, and there is heterogeneity in the nature of enterprises, but the innovation efficiency is not affected. Second, the period expense rate will lead to digital transformation, having different effects on the quantity and efficiency of technological innovation of agricultural enterprises. Third, the impact of digital transformation on the technological innovation efficiency of agricultural enterprises has a significant single threshold effect, and when the period expense rate is less than the threshold, the digital transformation has a significant role in promotion.

Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.