Dianchi Lake, as a typical eutrophic lake on the Yunnan-Guizhou Plateau, has fragmented and scattered cyanobacterial bloom patches, making it difficult to accurately capture through traditional monitoring methods. Therefore, there is an urgent need to utilize medium and high-resolution remote sensing image technology for the precise identification and long-term dynamic monitoring of the bloom, to reveal its spatiotemporal change characteristics and response mechanisms, thereby providing scientific support for the precise governance of Dianchi Lake and regional ecological protection. Based on 378 multi-source remote sensing images from GF-1, Sentinel-2, and Landsat 8 spanning 2014 to 2025, this study employs the previously developed lightweight deep learning model MBAUNet to precisely extract the long-term bloom in Dianchi Lake. The spatiotemporal change characteristics of the bloom over the past 12 years are then revealed, and the influencing factors of its outbreak are explored. The results show: (1) The collaborative use of multiple remote sensing sources effectively improves the temporal continuity of bloom monitoring, increasing the monthly time coverage to 82.64%, which is significantly higher than that of single data sources (63.89% for GF-1, 42.36% for Sentinel-2, and 41.67% for Landsat 8). This multi-source approach effectively reduces the monitoring gaps caused by single sensor revisit cycles, cloud and rain coverage, and data loss. (2) In terms of time, the bloom shows significant interannual fluctuations and seasonal characteristics, generally exhibiting an initial increase, followed by a decrease and subsequent minor fluctuations. Seasonally, summer and autumn are the core outbreak periods, with the bloom reaching its peak in August. The intensity weakens in winter and spring, dropping to its lowest level in March. Spatially, the bloom distribution shows a "high in the north, low in the south, high in the east, and low in the west" pattern. The Xinghai Bay and its adjacent waters in the north are the core areas with high bloom occurrence. From 2021 to 2025, the overall bloom coverage and outbreak intensity have returned to a lower level. (3) Meteorological and hydrological conditions, as well as nutrient salt levels, affect the short-term fluctuations of the bloom. Source control and ecological governance are important reasons for the decline in the size of the bloom after 2018, but residual nutrient salts, local pollution input, and climate fluctuations may still trigger periodic rebounds. The research results can provide a scientific basis for the long-term dynamic monitoring, risk warning, and ecological governance of Dianchi Lake.