In order to solve the index calculation problems of high complexity, strong completeness and low efficiency in the filed of financial foreign exchange market monitoring, a novel distributed computing framework for foreign exchange market monitoring based on Spark big data structure was proposed. Firstly, the business characteristics and existing technology framework for foreign exchange market monitoring were analyzed and summarized. Secondly, the foreign exchange business features of single-market multi-indicator and multi-market multi-indicator were considered. Finally, based on Spark's Directed Acyclic Graph (DAG) job scheduling mechanism and resource scheduling pool isolation mechanism of YARN (Yet Another Recourse Negotiator), the Market-level DAG (M-DAG) model and the market-level resource allocation strategy named M-YARN (Market-level YARN) model were proposed, respectively. The experimental results show that, the performance of the proposed distributed computing framework for foreign exchange market monitoring improves the performance by more than 80% compared to the traditional technology framework, and can effectively guarantee the completeness, accuracy and timeliness of foreign exchange market monitoring indicator calculation under the background of big data.

Aiming at the low prediction effect caused by single data source in the current RMB exchange rate forecast research, a forecast technology based on Internet public opinion intensity was proposed. By comparing and analyzing various data sources, the forecast error of RMB exchange rate was effectively reduced. Firstly, the Internet foreign exchange news data and historical market data were fused, and the multi-source text data were converted into the computable vectors. Secondly, five feature combinations based on sentiment feature vectors were constructed and compared, and the feature combination embedded with intensity of Internet public opinion was given as the input of forecast models. Finally, a temporal sliding window of foreign exchange public opinion data was designed, and an exchange rate forecast model based on machine learning was built. Experimental results show that feature combination embedded with Internet public opinion outperforms the feature combination without public opinion by 9.8% and 16.2% in Root Mean Squared Error (RMSE) and Mean Squared Error (MAE). At the same time, the forecast model based on Long Short-Term Memory network (LSTM) is better than that based on Support Vector Regression (SVR), Decision Tree regression (DT) and Deep Neural Network (DNN).

In terms of the evolution of complex dynamics in the dissemination of public opinion, a dynamic evolution model was proposed based on transmission dynamics. Firstly, the models of public opinion and its evolution were constructed and the static solution was obtained through equation transformation. Secondly, the Fokker-Planck equation was introduced to analyze the asymptotic behavior of public opinion evolution, getting the steady-state solution and solving it. In that case, the correlation between the complex network and the model was built and the experiment objective of simulation research was put forward. Finally, through the simulation analysis of the public opinion evolution model and the public opinion model with the Fokker-Planck equation, and the empirical analysis of real micro-blog public opinion data, the essence of the dissemination and evolution of public opinion in the complex network was studied. The results show that the asymptotic behavior of public opinion network evolution is consistent with the distribution of degrees and the connection way of network public opinion dissemination is influenced by nodes. The model can describe the dynamic behavior in the formation and evolution of micro-blog public opinion network.