At present, the pressure on China’s economic development is increasing day by day due to the profound changes in the internal and external environment. The global economic pattern is undergoing significant changes in terms of the external environment. Adjusting and optimizing the industrial structure will aid in achieving the goal of facilitating transformation through steady growth in the short term. Meanwhile, it will also accelerate sustainable economic development. In this study, the relevant theories of industrial structure optimization are described based on the impact of wireless mobile networks and the Internet of things (IoT) industry. Based on the gray correlation degree, the high- and new-tech industries under the development of the IoT industry are analyzed and the impact of optimization of the high- and new-tech industry structure is investigated. The results show that the development of the IoT industry has driven the development of the high- and new-tech industry. The gray correlation between the development of the IoT industry and the high- and new-tech industry obtained is 0.64, indicating a strong correlation. The average output share of the electronic computer and office equipment manufacturing industries is 47.09%. The average output ratio of the industrial structure optimization of the electronics and communication manufacturing industry is 42.55%. Moreover, the proportion of the output of medical manufacturing and medical equipment and instrument manufacturing industrial structure optimization is small, 15.63% and 10.54%, respectively. The results have significant value in the research on the impact of the development of the IoT industry on the high- and new-tech industry under the wireless mobile network and the effect of its industrial structure optimization.

This study constructs an index system for the conversion of new and old kinetic energy (NOKE) from three dimensions of electricity, economy, and energy efficiency in the cloud computing environment. First, an annual evaluation model is constructed based on the conversion of NOKE. Second, an evaluation model based on decision-making trial and evaluation laboratory (DEMATEL)-analytic network process (ANP) and improved grey relational method is established. Finally, the annual evaluation is carried out through examples. The results show that the conversion effect of NOKE in Liaoning Province is 0.7956 in 2019, 0.803 in 2020, and 0.9448 in 2021. The evaluation value increases year by year, indicating that the conversion effect of NOKE in Liaoning Province is becoming more and more significant. A series of NOKE conversion measures have achieved positive results. This study provides the theoretical basis and practical guidance for China to promote the transformation of NOKE.

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Debt financing is one of the important financing channel for high-tech company. However, Low debt financing efficiency is one of the reasons hindering the development of Chinese high-tech companies, which affect their R&D and technological achievements. Thus, We selects the financial data of high-tech companies which listed in GEM from 2015 to 2017, and evaluates the debt financing efficiency of 48 high-tech companies. The research shows that most high-tech companies' debt financing efficiency was not effective. Besides, debt financing efficiency of high-tech companies was on upward trend these three years. Third, improper company & financing scale and the growth of intangible-assets affect financing efficiency of high-tech company. Finally, we make suggestions for the development of Chinese high-tech industry.

Real estate companies are one of the most active forces in economic activities and occupy a large market share in China. Data of real estate listed companies from 2016 to 2018 are selected as samples to be listed on the Shanghai and Shenzhen stock exchanges. This paper studies by establishing panel data model, and used Stata15 software to empirically test different debt structures and whether debt levels affect performance. By processing the data, we can know that the higher the level of debt, the lower the company's performance, and performance will also improve as the proportion of short-term liabilities in the debt structure increases. The conclusion of this text shows that under the macro-economic background of deleveraging in China, real estate companies should reduce overall debt levels to reduce financial risks, and actively respond to national policies. In the structure of liabilities, the proportion of current liabilities should be appropriately increased to restrain the agent, reduce the risk investment behavior, and increase the business performance.