Design and Implementation of Intelligent Information System Based on Hybrid Neural Network

Traditional information systems often rely on pre-set rules and extensive human intervention, making them inadequate in dealing with complex and ever-changing real-world situations. Such systems commonly suffer from issues such as delayed response and insufficient decision-making accuracy. To overcome the above limitations, this study designed and implemented an intelligent decision-making information system that integrates a hybrid neural network architecture. This system fully incorporates the strengths of convolutional neural networks in local feature extraction, combines the modeling ability of long short-term memory networks for time series data, and introduces reinforcement learning mechanisms to dynamically adjust strategies. For the specific application scenarios of intelligent decision-making in enterprise supply chain, this system realizes the autonomous operation of the entire process from data collection, feature analysis, dynamic optimization to decision generation. To verify its effectiveness, experimental tests were conducted on actual supply chain data from a manufacturing enterprise. The results show that in the demand forecasting task, the accuracy of the system reaches 92.3%, which is 18.5% higher than the traditional ARIMA model; The inventory turnover rate has increased by 23%; Faced with abnormal orders, the system response time is only 1.2 seconds, which is 8 times faster than manual processing speed; The user satisfaction also reached 4.7 points (out of 5 points).