The investigation of Ecological Agriculture (EA) patterns can reveal the differences, aggregation, and diversity of agriculture development, providing specific paths in agriculture development and environment protection in order to achieve the Sustainable Development Goals. Although field surveys, literature analysis, and administrative statistical methods can be employed to comprehensively investigate EA records and determine EA distributions, they still rely on manual operations that are generally unable to support the rapid and large-scale identification of EA patterns required by current agricultural sustainable researches. To address this issue, this paper proposes a novel and rapid approach for Ecological Agriculture Pattern Investigation Based on Web-text (WEAPI), with the ability to automatically acquire EA pattern records including pattern type, occurrence time, precise location, and other relevant information. The proposed method is employed in a national scale case study to investigate trends in Chinese Ecological Agriculture (CEA). Results reveal the ability of WEAPI to detect new trends in CEA via the latest news, as well as the corresponding distributions. The WEAPI method can also exhibit the unknown patterns of the current Chinese agriculture development. Further validation experiments demonstrate the proposed method to achieve over 95% precision in the pattern parse processes and an 87% coverage rate at the town level of the official CEA pattern list. Moreover, WEAPI can also provide dynamic analyses on the evolution of the EA patterns. Despite limitations under sparse records in partial classes, the results reveal WEAPI to be a promising and powerful tool for agricultural research and agricultural development planning.