To evaluate the overseas investment risks of enterprises and expand the application and development of deep learning methods in risk assessment, 15 national clusters are utilized as samples to analyze and discuss the overseas investment risk indicators of enterprises. First, based on the indicator system of overseas investment risks, five major types of investment risks are identified. Second, the Deep Neural Network (DNN) is introduced; a risk evaluation model is constructed for enterprise overseas investment. Finally, the investment attractiveness index in the Fraser risk assessment learning label is adopted as the evaluation results of the model. According to the classification of risks, the model is trained and its performance is tested. The results show that the major source of overseas investment risks includes basic resources, political systems, economic and financial development, and environmental protection. The corresponding risk score is high. North American country clusters and Oceanian country clusters have lower investment risks, while the investment risks in Africa, Latin America, and Asia are affected by multiple factors of the specific cities. This is closely related to the resources and legal systems possessed by the country clusters. This is of great significance for enterprises to conduct risk assessment in overseas investment.

An enterprise is often faced with a large amount of financial information and data information. It is inefficient to rely solely on manual work, and the accuracy is difficult to guarantee. For the multisource data of corporate finance, it is more difficult for financial personnel to accurately analyze the connections between the data. For the multisource financial data of enterprise, this is also a time-consuming and laborious task for financial personnel. At the same time, it is difficult to find the correlation between multiple sources of data and then formulate financial data that guides the development of the enterprise. With the advancement of intelligent algorithms, an intelligent classification algorithm similar to the SAS model has emerged, which can realize the intelligent classification of enterprise financial multisource data and accurately predict the future development trend, which is extremely beneficial to the development and performance of the enterprise. This article mainly combines the financial intelligence classification model SAS with clustering and decision tree methods to classify the financial multisource information and uses the neural network method to carry out the future development trend of corporate finance. The research results show that the maximum error of enterprise financial classification after using the intelligent classification method is only 3.71% and that the forecast error of the future development trend of enterprise finance is only 1.77%. This is an acceptable error range, and this intelligent classification method is also greatly improving the efficiency of corporate financial management.