A Review of Stock Price Prediction Research Based on Artificial Intelligence and Big Data Technology

Stock price prediction in the financial sector has long been a hot topic of research. In recent years, with the continuous advancement of artificial intelligence and big data technologies, stock price prediction has gradually shifted from traditional statistical modeling paradigms toward an intelligent direction driven by multi-source heterogeneous data and dominated by deep learning. This study systematically reviews the recent research progress in the application of artificial intelligence and big data technologies to stock price prediction. It analyzes the field from multiple dimensions, including feature construction and fusion strategies, the evolution of modeling methods, and the application of big data processing frameworks. The study summarizes the current mainstream research paths and typical achievements, and discusses the challenges and constraints faced by various methods in practical applications. Additionally, this study summarizes the existing research findings and conducts an in-depth analysis of the limitations in the research.