With the continuous deepening of economic and social development and reform, the problems in rural financial development have gradually exposed. Therefore, in order to speed up the rural economic development and promote the construction of new rural areas. It is very necessary and urgent to promote the innovation of rural financial services and create a modern rural financial service platform. In order to solve the problems of information collection, processing and integration of rural financial information service platform in China, the diversification, personalization, timeliness and accuracy of information demand are difficult to be met, and the organization and operation mode of the platform are not perfect. In this paper, based on the intelligent sensor, the whole digital transformation is realized through the reference of big data. Based on this, this paper establishes the research model of rural financial information service platform under the smart financial environment. From the current situation of the construction and application of rural financial information service platform in China, it studies the basic situation of the construction of rural financial information service platform in China from three aspects of functional scope, service mode and operation mode, and draws the improvement conclusion. The results show that the efficiency of rural financial information service is increased by 20% after using the improved method in this paper, which has certain use value.

With the rapid development of the market economy, there are more and more projects in the financial industry, and their complexity and technical requirements are getting higher and higher. The development of computer technology has promoted the birth of robot consultants, and it is of great significance to use robot consultants to manage and supervise financial industry projects. In order to further analyze the development and supervision of robo-advisors under the digital inclusive financial system, this paper uses complex systems and clustering algorithms as technical support to carry out research. First, the traditional K-means algorithm is used to select the initial clustering center, to improve the noise and outlier processing capabilities, and to build a data mining system based on the improved algorithm. Then, a product design model for robo-advisors is built and the risks of robo-advisors are analyzed from three aspects: technology, market, and law. Analyzing the performance of the improved K-means algorithm, in the operation of the experimental dataset B, the accuracy of the clustering result after 6 iterations reached 97.08%, which shows that the algorithm has good performance. During the trial operation of the data mining system, the four types of customers of financial institutions were accurately clustered, and it was concluded that the main type of customers who brought benefits to financial institutions was high-income customers accounting for 10.75%. Robo-advisory product models are used to build five risk-level investment portfolios and conduct risk backtests. Except for the growth and income portfolio, other portfolios have consistently outperformed the performance benchmark during the analyzed time period. Running the research system of this paper in a financial institution, comparing the capital budget before and after the operation, found that the system can improve the accuracy of the budget and reduce the risk of the robo-advisor for the financial institution.

This paper evaluates whether globalization has led to greater sensitivity of Chinese consumer price inflation to the global output gap. The empirical analysis uses quarterly data over the period 1995-2012. The global output gap is measured by weighted output gap of China?s top eighteen trading partners. Estimating Phillips curve models and vector autoregressive models, we find that global capacity constraints have both explanatory and predictive power for domestic consumer?s price inflation in China. Therefore, the central bank of China should react to developments in global output gaps.

The study investigates the influence of the COVID-19 on the rate of R&D investment and foreign exchange development of China's most important emerging industry firms. From 2010 to 2020, data were collected from 26 locations across China, focusing on seven different types of critical creating companies. To analyze the data, we have applied Fourier Increased Unit Root Test, Granger causality assessments test, Pattern Assessment test, Poisson pseudo most excellent probability (PPML) approach, Wald test, and Regression analysis test. The results of the tests reveal a clear underlying association among COVID-19 relates Chinese exports and imports. COVID-19's instant effects on imports and exports lack working capital have been calculated, but the short-term, medium-to-long-term products are composite and unidentified. The article result main results are following: (i) The COVID-19 impacts the R&D investment is main industries like as high-end equipment industry, new materials industry, and new-era data innovation. (ii) The COVID-19 highly affects the imports and exports development network of Chinese strategic emerging industries which emphasizes cross-industry grouping features. The study provides the guidance to the future researchers to focus on COVID-19 affects on the strategic emerging industries of developed and underdeveloped countries to determine of foreign direct investment inflow and unemployment growth rates.JEL: G20, O10, O40

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