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 For instance, useless player data may reduce the effectiveness of Character AI, outdated match data may compromise the timeliness of intelligent broadcasting, and fabricated officiating data may undermine the fairness of AI refereeing.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.