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Abstract With the wide application of big data technology and the continuous innovation of the financial industry, the development of Internet finance has become irreversible. The new financial model has improved the efficiency of financial services and formed new ways of payment and transaction. The traditional credit information system has been unable to meet the needs of the development of the financial industry. The research methods adopted in this paper mainly include literature research, case analysis and comparative analysis. The development process, current situation and characteristics of China's personal credit system, as well as the development process and current situation of China's personal credit system under Internet finance are analyzed. It also points out the main problems faced by China's personal credit reporting system under the Internet finance. Then, aiming at the specific problems faced by China's personal credit reporting system under Internet finance, this paper makes case studies of representative market-oriented credit reporting institutions such as Beijing, Tianjin and Hebei, and uses convolutional neural network to analyze the specific practices of these individual credit reporting institutions and achieves results. At the same time, it compares how different credit agencies make up for the shortcomings of personal credit reporting system. Finally, it draws the conclusion of perfecting the personal credit system of our country under the Internet finance, points out the new problems that may be brought by the superposition of various solutions, and puts forward the countermeasures and suggestions of perfecting the personal credit system of our country under the Internet finance. [ABSTRACT FROM AUTHOR]

Urban agglomerations are considered as significant space typologies in the post globalization & digitalization era. The spatial linkage intensity of cities in urban agglomeration is an important basis for evaluating the development and compactness of urban agglomerations. The requirements associated with the major national strategy to achieve the coordinated development of the Beijing-Tianjin-Hebei (BTH) region and the research methods made possible by Big Data in the Internet era have created the realistic possibility of revealing the strength of spatial networks and the spatial differentiation rules of urban agglomerations. This study uses Web Crawler to obtain 500,000 sets of Weibo data in 13 cities of the BTH urban agglomeration. Three criteria and nine indicators are used to construct an index system and a model to quantitatively evaluate the strength of spatial networks in the BTH urban agglomeration. The results show that spatial network connections between cities in the urban agglomeration are not strong overall, reflecting the limited development of urban agglomeration; the spatial networks in the urban agglomeration have hierarchical and centralized characteristics, which reflect the imbalanced development of the city network; Beijing, Tianjin, and Shijiazhuang are the centers of the social network connections in the BTH urban agglomeration, which means that Weibo's online space reinforces the existing urban system; there is a positive correlation between network spatial connectivity and the hierarchy of cities in the urban agglomeration, where cities with higher levels would have more urban network connections; Generally, Weibo network connections are stronger than economic or transport links between the cities, and the development of information technology may reduce the disparity of regional development. This research innovatively uses social network big data to reveal the strength of spatial network connections and spatial differentiation rules in the urban agglomeration. The methodology provided in this paper is systematic enough for generalization. • The spatial linkage intensity is an important basis for evaluating the development and compactness of urban agglomerations. • We construct a new index system using network big data to measure the strength of connection between cities. • The methodology can be applied to other regions when examining social network big data from Facebook or Twitter. [ABSTRACT FROM AUTHOR]