Design and Implementation of Financial Investment Decision Aid System Based on Learning Analysis Technology

Applying learning analytics to the securities sector to explore the development patterns of China's securities industry is a significant innovation in technological advancement within this field. This introduction outlines the concept of securities big data and summarizes its applications in the securities sector, including transaction monitoring, financial analysis, panic index analysis, public opinion heat analysis, personalized services, forecasting, and quantitative investment. It also analyzes the primary learning models currently used for processing securities big data, such as transaction monitoring algorithms, financial analysis algorithms, panic index analysis algorithms, sentiment analysis algorithms, and algorithms for forecasting and quantitative investment. Furthermore, it provides a detailed discussion on machine learning prediction algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Bayesian Neural Networks (BNN), and the optimization of BP Neural Networks through Genetic Algorithms (GA). A comparison is made between traditional time series prediction models and machine learning-based prediction models in terms of their advantages and disadvantages. Lastly, the paper offers an outlook and summary for the future of the securities sector, highlighting the increasing application of big data technologies in this field and emphasizing that using machine learning algorithms to predict securities market trends is a key research direction and hotspot.