This study was conducted to analyze the perspectives of transnational media reporting on Sino-US trade. News reports from Associated Press and Xinhua News Agency were collected from March 20, 2018, to October 31, 2018. Text mining and frequency analysis was performed by Python to examine significant keywords from the news reports. Eigenvector centralities and Modularity analysis were conducted by Gephi. QAP correlation analyses were conducted by Ucinet. As a result, Associated Press and Xinhua News Agency had different clusters. There was a significant correlation between the Xinhua News Agency Semantic Network and the Associated Press Semantic Network. This study supports the two perspectives of reporting from different aspects. Xinhua News Agency involved four sub-topics when reporting Sino-US trade frictions, namely China-related clusters, US-related clusters, trade-related clusters, and tariff-related clusters. The Associated Press involved three sub-topics, namely China-US Trade-related clusters, global economy-related clusters, and tariff-related clusters. Xinhua News Agency focused on China-related clusters, while Associated Press focused on Sino-US trade-related clusters. The network structure of Xinhua News Agency and AP shows a positive correlation, which to some extent indicates the global perspectives of Chinese and American media reporting. The division of the two network clusters and the difference in the choice and prominence of the words in the network indicates that the Chinese and American media are still affected by the political stance and culture when constructing the framework strategy. Based on these results, valuable implications are provided for media reporting across countries.