Analysis on block chain financial transaction under artificial neural network of deep learning

Abstract

In order to conduct an in-depth study on financial transactions of block chain, the classical back propagation (BP) neural network based on the artificial neural network (ANN) model is selected, and its propagation mode, weight change, and learning process are analyzed. For the problem of slow convergence speed and local minimum value of BP neural network, based on the idea of deep learning, the initial value and training step are changed by auto-encoder and restricted Boltzmann machine, and the theory is analyzed. Taking the stock index futures trading in the block chain financial trading as an example, the stock price trading of stock index futures is studied using the two deep learning neural network models to predict the price changes. The results show that the auto-encoder, as an unsupervised learning system, performs better than the restricted Boltzmann machine in setting the initial weights and thresholds, with fewer iterations, faster convergence rate, and smaller convergence error. The results obtained by the auto-encoder can be used as initialization settings and data analysis. The prediction accuracy of the whole model is around 59%. When the transaction cost is not considered, the transaction can be conducted based on the prediction signal of the deep learning model. Therefore, deep learning neural network model can be applied to block chain financial transactions as a reference for financial transactions, which has a good practical significance for the development of this field.