Classification and Prediction of Trading Data in the Technology Finance Market Based on Support Vector Machine Algorithm

With the rapid development of the technology finance market, the complexity and diversity of trading data continue to increase, which poses huge challenges for investors and financial institutions. Traditional market analysis methods often rely on linear models and limited feature selection, resulting in many shortcomings in data quality, prediction accuracy, and real-time performance. This article conducts in-depth preprocessing of transaction data in the technology finance market, including data cleaning, missing value processing, and feature extraction. On this basis, SVM model is adopted to solve nonlinear problems through kernel techniques, and various technical indicators, market sentiment, and fundamental data are introduced to enhance the feature expression ability of the model. The experimental results show that the accuracy of the SVM-based model on the test set reaches 85%, and its predictive performance is significantly better than traditional methods. It can be seen that SVM-based analysis methods are more effective in capturing market dynamics and providing scientific basis for investment decisions.