This study focuses on the evolution of AI in the financial field, from AlphaGo to ChatGPT, as technology drives AI to penetrate into complex group game scenarios. Financial securities investment has become a core scenario for AI implementation due to its group game nature, but the existing evaluation system has shortcomings such as surface data listing, lack of time series and individual stock quantification, and lack of diagnosis of irrational behavior. To this end, a high-order feature user behavior prediction intelligent system integrating computer technology and financial scenarios has been constructed, which integrates four modules: structured data modeling, event information extraction, causal analysis, and feedback generation. The system quantifies operational behavior through SEA grid splitting and designs a attribution model for trend/stock selection/game ability; Adopting MRC framework for event extraction without trigger words and joint training scheme to extract event information; Quantify event impact using event analysis method and generate template comments based on user preferences. Research has found that the system has achieved a breakthrough in the quantitative system from the overall to individual stocks, filling the gap in causal attribution, promoting investors to transform towards a "data model dual drive" approach, demonstrating high-precision predictive capabilities (mAP verification) in financial digital management, strengthening decentralized architecture and transaction traceability functions, providing technical support for the digital upgrading of the financial industry, becoming a typical practice of technology integration in financial scenarios, laying the key technical foundation for future intelligent financial systems, ultimately optimizing decision-making efficiency, and promoting long-term stable development of the financial market.

Yongqiang Ma. A High-Level Feature User Behavior Prediction Intelligent System Integrating Computer Technology with Financial Scenarios. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 114-121. https://doi.org/10.38007/IJBDIT.2026.070114.

[1] Jin Li. Performance Analysis of Efficient Microservice Architecture in the Financial Industry. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 1-9.

[2] Yixian Jiang. Performance Optimization and Improvement of Advertising Machine Learning Platform Based on Distributed Systems. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 9-17

[3] Bukun Ren. Multimodal Learning Method for Cross-Modal Data Alignment and Retrieval. International Journal of Multimedia Computing (2026), Vol. 7, Issue 1: 1-8.

[4] Zhengle Wei. Research on Innovative Design of Financial Derivatives and Market Risk Management Strategies. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 19-27

[5] Yuhan Zhou. Green Bonds and Sustainable Financing Models in Energy Finance. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 28-35

[6] Yilin Fu. Research on the Application of Innovative Financial Technologies in Capital Market Risk Management. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 1-9

[7] Linwei Wu. Data Visualization and Decision Support Analysis Based on Tableau. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 10-18

[8] Xinran Tu. Resource Allocation Optimization and Cost Saving Analysis Based on Data Mining. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 1-9

[9] Wang, C. (2026). Research on the Control of Uncertainty Risks in Investment Decision-making by Financial Modeling.

[10] Shuang Yuan. Research on Abnormal Detection and Transaction Risk Management Based on Machine Learning. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 10-18

[11] Wang, Y. (2026). Research on Optimization of Neuromuscular Rehabilitation Program Based on Physiological Assessment. European Journal of AI, Computing & Informatics, 2(1), 21-30.

[12] Cai, Y. (2026). Design and Implementation of System Extensibility under High Concurrency Environment. International Journal of Engineering Advances, 3(1), 31-37.

[13] Liu, Y. (2026). The Application of Data-Driven Financial Risk Management in Multinational Enterprises. Economics and Management Innovation, 3(1), 20-26.

[14] Huang, J. (2026). Practice of Public Space Optimization and Functional Enhancement in Cultural Architecture. European Journal of Engineering and Technologies, 2(1), 9-21.

[15] Xu, D. (2026). AI-Driven Video Content Optimization Strategies for Immersive Media. European Journal of Engineering and Technologies, 2(1), 1-8.