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.

China’s “Belt & Road Initiative” has been proposed for several years, which has stimulated the economic and financial development of the countries alongside the “Belt & Road”. For a world’s leading energy consuming country, China tries to secure the energy supply from the resource-rich countries via oversea energy investment. In this paper, we propose a sequence to sequence (seq2seq) model to evaluate the energy investment risk of 50 countries alongside the “Belt & Road Initiative”. Specifically, we first build an indicator system mainly containing six factors. Then we adopt Bi-long-short term memory (Bi-LSTM) as encoder to process the historical statistics. Afterward, we use self-attention mechanism to assign the weights on the six factors of the indicator system. Finally we use a hierarchical convolution neural network decoder to generate the assessment results. Our findings indicate that resource potential and Chinese factor are the most important indicators. And through our thorough investigation, we find that Russia, Kazakhstan, Pakistan, United Arab Emirates, Saudi Arabia, Malaysia and Indonesia are the most recommended target countries for China’s oversea energy investment.