Hung, Yu-LingYu-LingHungJiang, Kun-LinKun-LinJiangChen, Yi-LiangYi-LiangChen張哲維2026-07-162026-07-1620260219-5194https://doi.org/10.1142/S0219519426400440https://ir.ntus.edu.tw/handle/123456789/131462This study employs a data-driven approach to evaluate player performance in Taiwan’s professional basketball league, P. LEAGUE+, and constructs an AI-integrated performance assessment model. Initially, web scraping techniques are used to extract structured and unstructured data — including basic statistics, advanced metrics, and textual content — from the official P. LEAGUE+ website, covering guards, forwards, and centers. Subsequently, a domain-specific AI vector database is established using Retrieval-Augmented Generation (RAG) for data cleansing. The model then integrates Entropy and TOPSIS decision analysis methods to develop an AI-Driven Comprehensive Performance Index for automated performance scoring. TAIDE-LX-7B is further applied for data inference and decision-making to identify the league’s Annual First Team. Using the 2021–2022 season data from six teams as a case study, the accuracy and validity of the proposed AI-driven model are verified. Results show that the AI-Driven Comprehensive Performance Index aligns with official postseason selections for guards and centers, while discrepancies in forward selection are attributed to league policies favoring domestic players.Sovereign AI databaseentropy TOPSISAI-driven comprehensive performance indexjournal article