Neural Network Intelligent Algorithm for Predicting Urbanized Economic Development

This article explored the application and effectiveness of the LSTM (Long Short-Term Memory) model algorithm in predicting and analyzing urbanization economic development. In recent years, accurately predicting the trend of urban economic development has become very important. Traditional economic forecasting methods suffer from limited data processing capabilities and insufficient model flexibility. For this purpose, this article designed an LSTM model algorithm to improve the accuracy and efficiency of urban economic development prediction. In the experimental stage, four experiments were designed to evaluate the predictive performance of urbanization economic development based on the LSTM model. In the benchmark model performance evaluation experiment, the AUC (Area Under the Curve) value of the LSTM model reached 0.92. In the time span prediction ability experiment, the mean square error of the LSTM model in each period ranged between 0.02 and 0.04. In the predictive evaluation experiment of data volume, when the data volume increased from 1000 to 10000, the accuracy of the LSTM model increased from 65% to 90%. In the final model parameter tuning experiment, by adjusting the LSTM model parameters, the accuracy of the model reached the highest value of 92%. From the data conclusion, it can be seen that the LSTM model is suitable for predicting urbanization and economic development tasks due to its excellent performance, and can provide strong data support for urban planning and economic policy formulation.