Application of Multimodal AI in CS:GO Match Analysis: From Data Parsing to Strategy Recommendation

Abstract

The application of multimodal AI in analyzing Counter-Strike: Global Offensive (CS:GO) matches represents an emerging intersection of computer vision, natural language processing, time-series modeling, and reinforcement learning within esports analytics. This paper proposes a comprehensive multimodal framework that integrates heterogeneous data sources - gameplay videos, voice communications, and structured statistics - to achieve efficient parsing, feature fusion, tactical pattern recognition, and intelligent strategy recommendation. By fusing multiple modalities, the system aims to automate and optimize tactical decision-making for professional teams, significantly improving the efficiency and accuracy of traditional manual analysis.