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

A Deep Learning Entity Extraction Model for Chinese Government Documents

In this paper, we propose a combined Whole-Word-Masking based Robustly Optimized BERT pretraining approach with dictionary embedding entities recognition model for Chinese documents. By using multiple feature vectors generated by such as Roberta and domain dictionaries as embedding layers, the contextual semantic information of the text is fully considered. Meanwhile, Bi-directional Long Short-Term Memory(BiLSTM) and a multi-head attention mechanism are used to learn the information of long-distance dependency of the text. We use conditional random field(CRF) to obtain the global optimal annotation sequence, which is expected to improve the performance of the model. In this paper, we conduct comparison experiments with five baseline-based methods in the official document dataset of government affairs domain. The Precision of the model is 91.8%, Recall is 90.5%, and F1 value is 91.1%, which are better than other baseline models, indicating that the proposed model is more accurate for recognizing named entities in government documents