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Amidst a transformative economic milieu in China, domestic enterprises are venturing into the global market, exposing them to intensified perils in international trade and investment. This research elucidates the international trade and investment (ITI) context within China, establishing criteria for ITI risk evaluation through an analytical exploration of international trade interactions. A methodology has been developed to quantify ITI risk, employing deep neural networks (DNNs), with a particular focus on the potential impact of edge cloud computing on China's trading economy. Through the utilization of convolutional neural networks (CNN), risks in China's trade and investment are appraised across various dimensions, exhibiting a noteworthy accuracy rate of 90.38%. It is identified that while CNN exhibits exemplary performance in estimating severe and high-risk scenarios, its efficacy diminishes when discerning general investment perils. The analysis underscores that a substantial portion of investments, constituting 14.8%, emanates from The Association of Southeast Asian Nations (ASEAN) and China, with market dynamics and macroeconomic conditions markedly influencing the risk associated with Chinese investments. By extending the utilization of deep learning (DL) in financial investments and integrating edge cloud computing, this investigation augments the capabilities for assessing China's ITI risk, providing a valuable resource for comprehending the ITI landscape within China.
Abid et al. (Sat,) studied this question.