Construction of International Trade Market Demand Forecasting and Risk Assessment Model based on Machine Learning Algorithm

In order to maintain the complexity and inclusiveness of the international business field, we must not hesitate to provide market and effective risks for international business research. The best approach is to adopt a universal and universal method, as Merkmale does not have linear and multidimensional behavior, so it is consistent with international business standards. This research began with relevant international journals such as The Economist, History of Economics, The Economist India, and Geopolitician. In Rahman's mathematical model, we can find the answer, which is the effectiveness and generality of the model. During the collaboration between Merkmale Technology and Pearson Korrelationships, the number of Korrelationships between Merkmale and Zielvariable was consistent. When one Schwelle company was fired, Merkmale company was also fired. Reistengen Doxidenna and Model Walden conducted an experiment, and well, this is the most suitable one. According to the LSTM model, the total squared error (MSE) is 66.3%, and the R² is 17.8%, which is close to the ARIMA model. However, based on the LSTM model, there is no linear relationship between a country's market and actions under the influence of the international handicraft market. How does market competition and risk affect, and how can it assist in the development of the international handicraft industry.