Digital inclusive finance is key to China’s agriculture and low-carbon economics. The panel data for China’s 30 provinces were chosen from 2011 to 2019. An SBM GML model was applied in the thesis to measure agricultural green total factor productivity (GTFP), and to determine how Digital Inclusive Finance would affect agricultural GTFP a two-way fixed effect model was created. This study found that, from 2011 to 2019, the advancement of Digital Inclusive Finance could effectively enhance and drive the continuous increase of agricultural GTFP in China. Specifically, agricultural GTFP is increased by 0.288% as a result of every 1% rise in the Digital Inclusive Finance index; Digital Inclusive Finance helps agricultural green technologies advance and become more effective. According to a mechanism test, Digital Inclusive Finance increases agricultural GTFP growth by improving green technology innovation. Further analysis shows that the development of agricultural GTFP is significantly related to the depth and digitalization of Digital Inclusive Finance, but not in terms of its breadth. The above findings provide new ideas and empirical evidence for revealing the connection among Digital Inclusive Finance and agricultural GTFP and, on this basis, designing and improving relevant policies.

This paper aims to study enterprise Financial Risk Management (FRM) through Big Data Mining (BDM) and explore effective FRM solutions by introducing information fusion technology. Specifically, big data technology, Support Vector Machine (SVM), Logistic regression, and information fusion approaches are employedto study the enterprise financial risks in‐depth.Among them, the selection offinancial risk indexes has a great impact on the monitoring results of the SVM‐based FRM model; the Logistic regression‐based FRM model can efficientlyclassify financial risks; theinformation fusion‐based FRM model uses a fusion algorithm to fuse different information sources. The results show that the SVM‐based and Logistic regression‐based FRM models can manage and classify enterprise financial risks effectively in practice, with a classification accuracy of 90.22% and 90.88%, respectively; by comparison, the information fusion‐based FRM modelbeats SVM‐based and Logistic regression‐based FRM models by presenting a classification accuracy as high as 95.18%. Therefore, it is concluded that the information fusion‐based FRM is better than the SVM‐based and Logistic regression‐based models; it can integrate and calculate multiple enterprise financial risk data from different sources and obtain higher accuracy; besides, big data technology can provide important research methods for enterprise financial risk problems; SVM‐based FRM model and Logistic regression‐based FRM model can well classify enterprise financial risks, with relatively high accuracy.

Under the background of the rapid development of data informatization, the evaluation and analysis of enterprise management accounting risk are the key link of enterprise sustainable development. Based on the theoretical basis of enterprise management accounting risk analysis, this paper selects 11 representative corporate financial risk indicators from financial risk indicators through correlation analysis and then selects 8 key indicators that affect corporate financial risk through association rules, to assess and analyze the company’s financial risks. Finally, this paper takes 20 ST companies in China as the research object to carry out empirical research. According to the assessment and analysis of financial risks in the company’s management accounting, it proposes measures from four modules to strengthen the company’s ability to resist risks.

A BP neural network-based model is proposed to study corporate financial risk analysis and internal accounting management. Using MATLAB software and the BP neural network model, it is possible to obtain enterprise financing risk situations over a period by simulating and predicting enterprise financing risks by creating an early warning model for enterprise financing risks. Finally, from the point of view of the company's internal and external operations, the company's financial risk prevention measures and proposals are proposed to improve the financing efficiency of the companies and to prevent financial risks. This study predicts the financing risk of companies listed on the Mongolian Stock Exchange and analyzes the causes of the risk status. According to the test results, the learning speeds for successive substitutions are as follows: 0.005, 0.01, 0.02, 0.03, and 0.04. Finally, it was found that the error was minimal and the stability was best when the learning speed was exactly 0.01. The error is 0.0031011, and the step size is 157, which is only slightly lower than the target error value, which indicates that the learning speed is good. In addition, the novelty of this study is the use of the BP neural network model to conduct an early warning study of corporate financial risks. The BP neural network assessment model for corporate lending risk in this document is highly accurate. In addition to providing theoretical insights to researchers, it can be a good tool for banks to realistically assess the credit risk of SME supply chain financing.

With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.