Application of Back Propagation Neural Network Method in Digital Economy Prediction

The Evaluation Index System (EIS) in present phase of Chine FinTech enterprises is determined through taking effect of digital economy on growth of FinTech. Particularly, Back Propagation (BP) neural network is developed for prediction method depending on functional information. The 6 China State-Owned Enterprises (SOEs) are chosen for stock price prediction by empirical evaluation. The 6 SOEs are Petro China, SINOPEC group, Huadian Energy, National Power, China Shenhua and Chinese Architecture. The Back Propagation (BP) Neural Network (NN) is used for Predicting the stock market price in digital economy. The grey relation analysis for screening feature variables, integrated with BPNN for improve the prediction results. The BPNN method was evaluated with performance indicators of Mean Absolute Prediction Error (MAPE), Mean Square Error (MSE), Mean Absolute Error (MSE) and Correlation Coefficient (R). The BPNN method attained less error rate of 3.45 MAPE, 0.52 MSE, 0.43 MAE and 0.31 R that is effective than other exiting methods like Artificial Neural Network (ANN) and Decision Tree (DT).