###### Abstract  
In 2013, China proposed the \"Belt & Road Initiative\" which aims to invest the \"Belt & Road\" countries so as to help them develop their infrastructure and economy. China consumes the largest part of fossil energy of the whole world, so it is China's priority to consider its energy supplying security. Therefore, it becomes urgent for China to invest the \"Belt & Road\" countries' energy facilities. There comes a question: how to evaluate the overseas energy investment risk? To answer this question, this paper proposes a deep learning method to assess such risk of the 50 \"Belt & Road\" countries. Specifically, this paper first proposes an indicator system in which 6 main factors are separated into 36 sub-factors. This paper makes use of hierarchical convolution neural networks (CNN) to encode the historical statistics. The hierarchical structure could help CNN handle the long historical statistics more effectively and efficiently. Afterward, this paper leverages the self-attention layer to calculate the weights of each sub-factor. It could be observed that the resource potential is the most important indicator, while \"years of China's diplomatic relations\" is the most important sub-indicator. Finally, we use a conditional random field (CRF) layer and softmax layer to compute the assessment scores of each country. Based on the experimental results, this paper suggests Russia, United Arab Emirates (UAE), Malaysia, Saudi Arabia, Pakistan, Indonesia, and Kazakhstan to be China's most reliable choices for energy investment. energy investment risk assessment; seq2 seq framework; Belt & Road initiative +
Footnote †: journal:  
face unseen and unpredicted high risks when investing those \"Belt & Road\" countries as the majority of those countries are developing countries. For instance, the Middle East countries have abundant fossil energy; however, they tend to have unstable political environment and a higher risk of having a war comparing other regions of the world. Such potential risks may cause the China's investment efforts in vain. Therefore, how to assess the oversea energy investment of China is the focus of this paper. There do exist some studies considering the assessment of energy investment in various aspects. For instance, Reference [5] focuses on the energy facilities, like the energy grid, while Reference [6] works on the energy transmission risk. Ref. [7] takes the uncertain factors into consideration, like the price of fossil energy, electricity, CO\({}_{2}\), and so on. These methods only focus on the micro-level factors that may have influence on the energy investment. As pointed out by Reference [8], to have a comprehensive evaluation of China's oversea energy investment, scholars should take both micro-level and macro-level factors into consideration. In terms of macro-level factors, there are some international organizations rating the countries based on macroscopic quantitative analysis, such as Moody's, Standard & Poor's, and International Country Risk Guide (ICRG) ([[https://epub.prsgroup.com/products/international-country-risk-guide-icrg](https://epub.prsgroup.com/products/international-country-risk-guide-icrg)]([https://epub.prsgroup.com/products/international-country-risk-guide-icrg](https://epub.prsgroup.com/products/international-country-risk-guide-icrg)) (accessed on 1 September 2020)). ICRG considers three macroscopic factors, including politics, economy, and finance. However, those ratings are not directly related to the fossil energy. Ref. [9], hence, proposes to consider the exportation of crude oil as a national factor via decomposition hybrid approach. However, while most scholars only research the general energy investment risk assessment, few of them analyze the issue from China's perspective [10; 11; 12]. This paper aims to fill this gap by conducting a thorough study on China's oversea energy investment. In addition to the above discussed factors, this paper includes more related factors of the energy investment.