Blockchain financial investment based on deep learning network algorithm Abstract   In order to study the use of indepth learning to process financial data, it is proposed that the related technology of neural network and deep learning can be applied to financial data, and real stock index and futures data can be used to explore the application effect of neural network and indepth learning. Firstly, the theory and model of indepth learning and neural network are introduced in detail. Secondly, a simple neural network and indepth learning model are used in the stock index and futures price forecast. The data used in the input of the model include the price of a stock in the current trading and some data indicators, and the closing price of a stock in the next time. The decline will be reflected in output. If the output is up for 1 and down for 0, new data will be input after the training of the model. Finally, the output data can be compared with the real data to judge the application effect of the model, after comparing e and analyzing the application effect of the model. The results show that the research in this study can help investors to build an automated investment model and the investment strategy of the stock market. The construction can be used for reference to improve investorsxe2x80x99 investment strategy and return rate.

The Financial Data Prediction System Based on the Recurrent Neural Network This paper introduces the breakthrough achievements of the artificial intelligence in recent years, designs the system on the basis of RNN related technology, and then demonstrates the predictive effect of the system through the experimental group, and evaluates the predictive accuracy of the system by using the deep learning related evaluation algorithm. The experimental evaluation results show that the scheme of using the recurrent neural network to learn and analyze historical data and using its model to predict the future trend of the financial data has high reliability and accuracy. Therefore, the indepth learning has great potential in the field of the financial forecasting.