PurposeTo identify and analyze the occurrence of Internet financial market risk, data mining technology is combined with deep learning to process and analyze. The market risk management of the Internet is to improve the management level of Internet financial risk, improve the policy of Internet financial supervision and promote the healthy development of Internet finance.Design/methodology/approachIn this exploration, data mining technology is combined with deep learning to mine the Internet financial data, warn the potential risks in the market and provide targeted risk management measures. Therefore, in this article, to improve the application ability of data mining in dealing with Internet financial risk management, the radial basis function (RBF) neural network algorithm optimized by ant colony optimization (ACO) is proposed.FindingsThe results show that the actual error of the ACO optimized RBF neural network is 0.249, which is 0.149 different from the target error, indicating that the optimized algorithm can make the calculation results more accurate. The fitting results of the RBF neural network and ACO optimized RBF neural network for nonlinear function are compared. Compared with the performance of other algorithms, the error of ACO optimized RBF neural network is 0.249, the running time is 2.212 s, and the number of iterations is 36, which is far less than the actual results of the other two algorithms.Originality/valueThe optimized algorithm has a better spatial mapping and generalization ability and can get higher accuracy in short-term training. Therefore, the ACO optimized RBF neural network algorithm designed in this exploration has a high accuracy for the prediction of Internet financial market risk.

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Exports are a crucial driving force of China’s economic growth; however, researchers have yet to verify whether they can effectively improve the high-quality economic development. Based on interprovincial panel data from 2000 to 2019, in this study, we constructed a high-quality economic development indicator system through the entropy weight method, and we adopted the linear regression model and dynamic panel threshold model to empirically test the export trade effect on the high-quality economic development as well as its mechanism. We also probed the impact of the heterogeneous absorptive capacity on the high-quality economic development. According to our research findings, China’s export trade has substantially promoted the high-quality economic development level by, from the viewpoint of the action path, positively influencing the economic, open, and coordination subsystems. The influence of the export trade on the high-quality economic development has a substantial single-threshold effect on the heterogeneous absorptive capacity; that is, when the threshold variables that represent the regional absorptive capacity (the economic level, R&D intensity, and technological gap) are all higher than the threshold value, the export trade has a substantial positive impact on the high-quality economic development level. The research conclusions of this paper provide new ideas for the development of high-quality economic systems as well as a useful reference for China in its formulation of more targeted foreign trade policies.

The majority of the literature currently in existence on trade and pollution has concentrated on the analysis of both factors’ combined effects, and only a few studies have used heterogeneous environmental regulation as a starting point to investigate the underlying mechanisms of the impact of export trade on environmental pollution at the indirect level. We construct a mediating and moderating effect model using panel data from 30 provinces in China from 2002 to 2019 to investigate the mechanism of the effect of export trade on environmental pollution. Export trade produces large indirect inhibitory effects on environmental pollution only through market incentive-based restrictions, whereas the mediation impacts of government administrative and public monitoring laws are not significant. By interacting with elements such as technical innovation and energy structure, export trade can also negatively regulate its bad consequences on environmental degradation. According to the heterogeneity analysis’s findings, processing trade indirectly reduces pollution emissions by changing administrative rules and cutting emission costs, but general trade indirectly increases environmental pollution by favorably impacting market-based incentives regulations. The moderating effects of improving energy structures, industrial structure optimization, and R&D competition effects diminish the positive aggravating effect of general trade on pollution emissions, while processing trade has the opposite effect. The only means of controlling the harmful impact of processing trade on environmental degradation is through interaction with technical progress.

The purpose is to find out the problems existing in the consumption economy structure of the scenic spots and to promote the rationalization of the consumption economy of the scenic spots. Based on the analysis of the applicability of the backpropagation neural network (BPNN) model, it uses BPNN to analyze the economic development level of Overseas Chinese Town East (OCT East). Firstly, the weight of each index is determined by the Analytic Hierarchy Process (AHP), and the expected value of the comprehensive evaluation is obtained. Secondly, to ensure the validity of the evaluation model for the development level of the tourism complex, the BPNN model is trained and tested to enable it to be applied to the evaluation of the economic development level of OCT East. The development level of OCT East from 2012 to 2021 is divided into three stages: high, higher, and lower. The development characteristics and existing problems of the OCT East are analyzed, and the optimization strategy of the consumption economy of the scenic spots is put forward in a targeted manner. The research results manifest, that from 2012 to 2021, the development level index of OCT East increased from 0.2457 to 0.5304, and it was in a state of steady growth. In 2019, the development level index reached 0.6497, and it was upgraded to “high-level,” but the average development level index of OCT East was only 0.5662, and there was a lot of room for improvement. According to the divided evaluation indicators, the development level of OCT East is evaluated. In 2012, the development level was low. From 2013 to 2018, it was at a high level, and from 2019 to 2021, it was a high level of development. By studying the Tourism Consumption Structure (TCS) of scenic spots in the OCT East, the research method of the consumption economic structure has been expanded. Therefore, it not only provides a reference for optimizing the consumption of scenic spots, but also contributes to the progress of the social tourism economy.