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The research results of this paper provide new ideas and perspectives for the construction of trading market of China’s carbon emission rights, so as to better alleviate the difficulties in China’s energy conservation and emission reduction. Data evaluation and image analysis based on wavelet transform image processing technology and deep learning were used in the study. Moreover, wavelet transform was used to extract the edge, and compared with the results of several other methods for extracting edges, so as to further study and analyze the trend of carbon emissions trading. The fluctuations in the price of carbon emissions trading, the shift of state, and the period of ups and downs are affected by many random factors. Through research algorithms, we can estimate the situation, seize opportunities, and forecast the prospects. In addition, based on the research, this paper obtains the relevant strategies for carbon emissions trading and value assessment in China.

The new method has been taken to describe the dynamic system of U.S. dollar-euro exchange rate and the monetary and credit policy of the Federal Reserve and European Central Bank. The Grove Tubman -Hartman Theorem has been applied to redesign the system of nonlinear differential equations, with data in two periods: The law of relationship changes between exchange rate and interest rates has been modeled. The empirical results show that the dynamics of these changes may have seven systems; this is based on the complex structure of the financial system. Research methods used can be applied for the development of program structure and prediction purposes of the international financial systeml.