Comparison of Prediction Model of Cooperation Risk of Multinational Enterprises Based on Deep Learning for International Policies

Abstract

The purpose is to study the effective risk management mode in the process of transnational cooperation of enterprises under the background of international policy and optimize the international cooperation strategy of enterprises. Firstly, the current situation of cross-cultural enterprise management models is compared and explored. Secondly, the advantage of efficient task offloading of edge cloud computing is used to extract the data information of multinational enterprises. Based on the extracted data, the loyalty of enterprise management is predicted through the deep learning (DL) model, and the prediction model of cooperation risk of multinational enterprises is implemented. Finally, the theory of game theory is applied in the calculation of profit distribution among multinational alliance enterprises, and the index evaluation system is established for quantitative and qualitative analysis. The results reveal that after 300 iterations, the accuracy loss of the traditional model is reduced to 0.3, while the accuracy loss of the prediction model established in this work is reduced to 0.15. Through the comparison between different models, it can be found that DL has an excellent effect on the risk prediction of enterprise strategic cooperation. This reveals that the key to transnational cooperation is to enhance the management ability of enterprises, thereby improving the competitiveness of enterprises. It can be concluded that DL and game theory are of practical value for reasonable resource allocation in transnational cooperation, to improve the risk management and prediction ability of enterprises.