Utilizing Internet Big Data and Machine Learning for Product Demand Forecasting and Analysis of Its Economic Benefits

In the context of digitalization and big data-driven advancements, the accuracy of demand forecasting in supply chain management has become a key competitive factor for businesses. This paper introduces a hybrid model combining Graph Convolutional Networks (GCN), Long Short-Term Memory networks (LSTM), and attention mechanisms, which enhances forecasting performance by integrating internet big data. The model extracts key information from multiple data sources, uses GCN to capture complex relationships within the supply chain, and employs LSTM for processing time-series data, while the attention mechanism boosts sensitivity to critical time points and relationships, significantly improving prediction accuracy. Moreover, the model optimizes production plans and inventory management, reduces the risk of supply chain disruptions, and enhances market adaptability and competitiveness

The Analysis of the Conversion of New and Old Kinetic Energy in Third-tier Cities from the Perspective of New Taxes: Taking City A as an Example

The economic adjustment function of taxation has played an important role in promoting the China’s conversion of new and old kinetic energy of enterprises. This study based on precious first-hand quarterly data of more than a hundred key tax source companies in City A from 2015 to the first quarter of 2018. The results show that government tax incentives and strengthened supervision have significantly promote the innovation level of enterprises; after the "replacing the business tax with a value-added tax", high labor costs of modern service hinder the speed of new and old kinetic energy conversion