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Nuclear power has gradually become one of the key parts in power supply structure due to its environmental benefits, effective productivity and steady operation. However, complicated system, high investment and derivative security issues make it difficult to develop in a large scale. Nowadays, faced with serious challenges from environment protection, fossil fuel lacking and upgrading of industrial structure, China are taking measures to promote nuclear power industry to realize sustainable development. China׳s nuclear power industry has achieved large achievements in industrial policy, technical and economic condition during the last three decades. However, there still exist problems impeding its future development. Therefore, this paper first analyzes the external environment and historical stages of nuclear power industry in China. Then, it summarizes its current situation and points out the existing problems. In the end, to deal with the above problems, this paper proposes relative solutions from the aspects of industrial distribution optimization, regional resources integration, technical standard system, industrial organization mode and so on.

As an emerging industry of energy conservation and environment protection, electric vehicle industry has broad prospects for development. But now, electric car industry is in the initial stage and the development environment is complex, so it is very necessary to study the development environment. This paper combines electric vehicle industry development environment evaluation index system with BP neural network to establish the electric vehicle industry evaluation model. Then the indicator score values as training samples obtained from the use of experts scoring method are imported to the BP neural network evaluation model. After training and testing the neural network, this paper compares the testing results with the results based on analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, to test the validity of the evaluation model. The result shows that evaluation model based on BP neural network can effectively improve the reliability and accuracy of evaluation result.