Using Improved BPNN to Predict the Time of Social Poverty Reduction under the Background of Internet

Abstract

The world has been overturned by the power of the Internet, and all walks of life are constantly changing with the trend of the times. Therefore, with the help of the effective technology and means of the Internet, people can start from a more complex perspective. Analyze the social environment and economic situation, and focus on poverty. By actively monitoring the number of poor people in society, we can grasp the actual situation of poverty. Starting from the depths of the problem, aiming at the characteristics of poor groups, this paper excavates the scale data of poor people. Finally, the improved BP neural network model is used to predict the time when the society gets rid of poverty and reduces poverty. The results show the following: (1) After P value judgment and T value test, the experiment determined 12 indicators and proportion of poverty. The most prominent indicators of contribution rate are T2, T4, and T5. The most significant changes in T test results were T8 and T12. (2) The reformed BP network has less iteration times, shorter training time, and higher prediction accuracy. The network converges 30 times, and the fitting accuracy reaches the best, and the ideal state is 0.000936578. Training the poverty alleviation data from 2016 to 2020 with the model, the final poverty alleviation work has achieved remarkable results. (3) According to the hidden layer nodes of neural network structure, the final number of hidden layer nodes is determined to be 11. (4) Comparing the absolute error and APE error of the prediction results. The predicted value is very close to the actual value, which can correctly reflect the mapping relationship of poverty reduction and the result is reliable. (5) Compare the predicted values of BPNN model before and after modification; it is found that the new BPNN model is more accurate in prediction. The predicted population is close to the actual population, and the error level curve approaches 0%. After empirical test, the improved BPNN model is accurate and effective. Through the poverty reduction prediction of the model, people can observe the actual situation more intuitively. By evaluating the effectiveness of social poverty alleviation, we can optimize the next poverty alleviation strategy and adjust the strategic policy.