大数据技术在税收预测中的应用研究

摘 要

随着数字经济的快速发展，大数据技术逐渐成为推动税收管理现代化的重要工具。本研究以税收预测为切入点，探讨如何利用大数据技术提升税收预测的精准性和效率。研究背景源于传统税收预测方法在数据处理能力和模型灵活性方面的局限性，难以满足现代税收管理对动态性和复杂性的要求。为此，本文旨在通过引入大数据技术优化税收预测流程，提高预测结果的科学性和实用性。研究采用多源数据融合、机器学习算法和时间序列分析等方法，构建了一个基于大数据的税收预测框架。该框架能够整合税务、经济、社会等多维度数据，并通过深度学习模型挖掘潜在规律，从而实现对税收收入的精准预测。实验结果表明，相较于传统方法，基于大数据技术的预测模型显著提升了预测精度，尤其是在处理非线性关系和不确定性因素时表现出更强的适应性。此外，研究还验证了不同算法在特定场景下的适用性，为实际应用提供了参考依据。本研究的主要创新点在于将大数据技术与税收预测深度融合，提出了一个多维度、动态化的预测体系，不仅增强了预测能力，还为税收政策制定和资源配置优化提供了有力支持。总体而言，本研究为税收管理领域的数字化转型提供了新的思路和技术支撑，具有重要的理论价值和实践意义。

With the rapid development of the digital economy, big data technology has gradually become a crucial tool for advancing the modernization of tax administration. This study focuses on tax forecasting as an entry point to explore how big data technology can enhance the accuracy and efficiency of tax predictions. The research background stems from the limitations of traditional tax forecasting methods in terms of data processing capabilities and model flexibility, which fail to meet the dynamic and complex requirements of modern tax management. To address these challenges, this paper aims to optimize the tax forecasting process by introducing big data technology, thereby improving the scientific validity and practical applicability of prediction outcomes. The study employs methods such as multi-source data fusion, machine learning algorithms, and time-series analysis to construct a big-data-based tax forecasting fr amework. This fr amework integrates multidimensional data from taxation, economics, and society, leveraging deep learning models to uncover latent patterns and achieve precise forecasts of tax revenues. Experimental results indicate that compared with traditional methods, the big-data-driven prediction model significantly improves forecasting accuracy, particularly demonstrating stronger adaptability in handling nonlinear relationships and uncertain factors. Additionally, the study validates the applicability of different algorithms in specific scenarios, providing reference for practical applications. A major innovation of this research lies in the deep integration of big data technology with tax forecasting, proposing a multidimensional and dynamic prediction system. This not only enhances forecasting capabilities but also provides strong support for the formulation of tax policies and the optimization of resource allocation. Overall, this study offers new insights and technical support for the digital transformation of tax management, holding significant theoretical value and practical implications.

Keywords: Big Data Technology; Tax Forecasting; Multi-Source Data Fusion; Machine Learning; Time Series Analysis


目  录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2大数据技术在税收预测中的理论基础 2
2.1大数据技术的核心概念 2
2.2税收预测的基本原理 3
2.3大数据与税收预测的结合机制 3
2.4数据驱动的税收预测模型框架 4
3大数据技术在税收预测中的关键技术应用 4
3.1数据采集与预处理方法 4
3.2数据挖掘算法的选择与优化 5
3.3机器学习在税收预测中的作用 5
3.4可视化技术对预测结果的支持 6
4大数据技术在税收预测中的实践案例分析 6
4.1实践案例的选取标准 6
4.2案例一：区域税收预测分析 7
4.3案例二：行业税收预测评估 7
4.4实践中遇到的问题与解决方案 8
结论 9
参考文献 10
致    谢 11