随着近年来人工智能技术的快速发展，其在电子竞技中已经或即将实现辅助训练、辅助裁判、智能播报等多项功能。然而，人工智能在驱动电子竞技运动发展的同时，也面临着相应的数据污染风险，如无用的玩家数据可能会降低角色AI的有效性，过时的赛况数据可能会损害智能播报的时效性，伪造的执裁数据可能会影响智能裁判的公正性等。面对这些风险，最为主要的应对之策即通过法律和政策的形式构建数据治理规则，在数据结构化、数据筛选和数据治理体系建立3个阶段分别建立标注类型规制、数据筛选规则、前置标准布控等多项机制，使采集到的电竞数据具有可用性，并增强其通用性，提取出满足特定条件的数据子集，最终通过电竞数据治理规则实现数据污染风险的全方位防范。

With the rapid development of artificial intelligence (AI) in recent years, it has already or is poised to implement multiple functions in e-sports, such as assisted training, assisted refereeing, and intelligent broadcasting. However, while AI drives the advancement of e-sports, it also faces corresponding risks of data pollution. For instance, useless player data may reduce the effectiveness of Character AI, outdated match data may compromise the timeliness of intelligent broadcasting, and fabricated officiating data may undermine the fairness of AI refereeing. To address these risks, the primary response strategy is to establish data governance rules in the form of laws and policies.. This involves establishing mechanisms such as the regulation of annotation types, data screening rules, and pre-set standard controls during three stages: data structuring, data screening, and the establishment of the data governance system. These measures ensure the usability and generalizability of collected e-sports data, extract data subsets that meet specific conditions, and ultimately achieve comprehensive prevention of data pollution risks through e-sports data governance rules.