Frequent outbreaks of Cladophora blooms in the newly formed littoral zone of Qinghai Lake have been observed due to the warming and humidification of the Qinghai-Tibet Plateau climate. Previous studies on the extraction of Cladophora blooms mainly relied on multi-source satellite remote sensing imagery. However, the limitations of image spatial resolution and mixed-pixel effects hindered the accurate identification of the true distribution and detailed features of the blooms. This study utilized low-altitude UAV imagery combined with the Attention DeepLab V3+ deep learning model to automatically extract Cladophora bloom features in Qinghai Lake. A comparative analysis was conducted with results derived from spectral indice and machine learning methods, and the differences between UAV imagery and optical satellite remote sensing imagery in extracting Cladophora blooms were explored. The results revealed the following: (1) Attention DeepLab V3+ could accurately detect Cladophora blooms without prior thresholds, achieving a kappa coefficient, precision, recall, and F1 score of 0.985, 0.969, 0.983, and 0.976, respectively. (2) Compared with existing methods, the model’s kappa coefficient and F1 score improved by 4.47%-29.75% and 6.35%-34.02%, respectively, demonstrating superior adaptability to complex bloom distribution patterns, especially in capturing boundary details and separating voids. (3) Optical satellite remote sensing imagery tended to overestimate Cladophora blooms in Qinghai Lake, with mean relative error values ranging from 5.5% to 323.47%. This study leveraged the high-resolution advantages of UAV imagery to provide technical support for accurately assessing the true distribution of Cladophora blooms in Qinghai Lake and laid a foundation for the monitoring and tracking of algal blooms features in other water bodies.